VSI-Bench-U — Gemma4-31B

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Keyframes

Question

Measuring from the closest point of each object, what is the distance between the sofa and the toilet (in meters)? These are frames of a video. Answer the question. For multiple-choice, answer with the option's letter from the given choices directly. For numerical questions, answer with a single number.

Ground truth 2.8

SpatialClaw 3.143 0.80

Agent wins

No-tool Baseline

Score 0.20

To determine the distance between the sofa and the toilet, we analyze the spatial layout provided in the video frames:

Locating the Toilet: In frames 6, 14, and 15, the toilet is visible inside the bathroom. It is positioned a short distance (approximately 1 to 1.5 meters) from the bathroom doorway.
Locating the Sofa: In frame 21, looking out from the bathroom into the adjacent room, a grey sofa is visible. The sofa is located a few meters (approximately 2 to 3 meters) away from the bathroom doorway.
Estimating the Total Distance: The distance from the toilet to the doorway is roughly 1.5 meters, and the distance from the doorway to the sofa is roughly 2.5 to 3 meters. Summing these distances gives an estimated total distance of approximately 4 to 4.5 meters.

Given the typical scale of a home layout and the visual evidence, the distance between the sofa and the toilet is approximately 4 meters.

Single-pass code

Score 0.00

Purpose

Identify the toilet and sofa, segment them, reconstruct the 3D scene, and calculate the distance between them.

Reasoning

1. **Object Identification**: I've identified the toilet in key frame #17 (InputImages[1122]) and the sofa in key frame #30 (InputImages[1914]). 2. **Segmentation**: I will use `SAM3.segment_video_by_text` to track the "toilet" and "sofa" across a range of frames. 3. **3D Reconstruction**: To get a metric distance, I need a 3D reconstruction. I'll select a subset of 64 frames spanning from the bathroom (where the toilet is) to the living room (where the sofa is), approximately from index 1100 to 2000. 4. **Distance Calculation**: I will find the 3D centroids of the masks for the toilet and sofa in frames where they are well-represented and calculate the Euclidean distance between them. Since the question asks for the distance between the *closest points*, I'll use the masked point clouds to find the minimum distance between the two sets of points.

Next Goal

Execute the reconstruction and distance calculation.

Code

import numpy as np

# 1. Define frames for reconstruction and segmentation
# We want to cover the area from the toilet (around frame 1122) to the sofa (around frame 1914)
start_idx = 1100
end_idx = 2000
# Subsample to 64 frames for reconstruction
recon_indices = np.linspace(start_idx, end_idx, 64).astype(int)
recon_frames = [InputImages[i] for i in recon_indices]

# 2. Reconstruct the scene
recon = tools.Reconstruct.Reconstruct(recon_frames)

# 3. Segment the objects
# We'll segment over the same range to ensure consistency
# Using segment_video_by_text for convenience
seg_toilet = tools.SAM3.segment_video_by_text(["toilet"], labels=["toilet"], start_frame=start_idx, end_frame=end_idx)
seg_sofa = tools.SAM3.segment_video_by_text(["sofa"], labels=["sofa"], start_frame=start_idx, end_frame=end_idx)

# 4. Find the best frames for each object to get points
# Toilet is best seen around frame 1122
# Sofa is best seen around frame 1914
# We need to find the absolute frame indices in the reconstruction
# Note: recon.frame_indices contains the absolute indices used for reconstruction.

# Get points for the toilet
toilet_points = []
for fi in recon.frame_indices:
    if fi in seg_toilet.frame_indices:
        mask = seg_toilet.get_mask(frame=fi, object='toilet')
        if mask.sum() > 100: # minimum size to be reliable
            pts = seg_toilet.get_masked_points(recon, frame=fi, object='toilet')
            if pts is not None:
                toilet_points.append(pts)

# Get points for the sofa
sofa_points = []
for fi in recon.frame_indices:
    if fi in seg_sofa.frame_indices:
        mask = seg_sofa.get_mask(frame=fi, object='sofa')
        if mask.sum() > 100:
            pts = seg_sofa.get_masked_points(recon, frame=fi, object='sofa')
            if pts is not None:
                sofa_points.append(pts)

if not toilet_points or not sofa_points:
    # Fallback: if video segmentation failed, try image segmentation on key frames
    # Toilet: InputImages[1122]
    # Sofa: InputImages[1914]
    # This is a simpler fallback
    print("Video segmentation failed or returned no points. Trying image segmentation.")
    
    # Re-do reconstruction if needed, but let's try to use the existing one if indices match
    # Actually, let's just use the centroids from the best frames if points are missing.
    pass

# Combine all points for each object
all_toilet_pts = np.concatenate(toilet_points, axis=0) if toilet_points else None
all_sofa_pts = np.concatenate(sofa_points, axis=0) if sofa_points else None

if all_toilet_pts is not None and all_sofa_pts is not None:
    # Compute minimum distance between the two point clouds
    # To avoid O(N*M) complexity, we can use a KDTree or just sample
    from scipy.spatial import cKDTree
    tree = cKDTree(all_toilet_pts)
    dist, _ = tree.query(all_sofa_pts, k=1)
    min_dist = np.min(dist)
    print(f"Minimum distance: {min_dist:.3f} meters")
    ReturnAnswer(float(min_dist))
else:
    # Final fallback: use centroids of the most confident frames
    # Let's try to get a centroid for each
    # Toilet in frame 1122
    # Sofa in frame 1914
    # We need these frames in the reconstruction. 
    # Let's ensure they are there or just use the closest reconstructed frame.
    
    # Find closest reconstructed frames
    f_toilet = 1122
    f_sofa = 1914
    
    # Find closest in recon.frame_indices
    idx_t = recon.frame_indices[np.argmin(np.abs(np.array(recon.frame_indices) - f_toilet))]
    idx_s = recon.frame_indices[np.argmin(np.abs(np.array(recon.frame_indices) - f_sofa))]
    
    # Segment just those frames
    seg_t = tools.SAM3.segment_image_by_text(InputImages[f_toilet], "toilet")
    seg_s = tools.SAM3.segment_image_by_text(InputImages[f_sofa], "sofa")
    
    # We need the reconstruction to have these frames to use get_centroid_3d
    # Since we used np.linspace, they might not be exactly there.
    # Let's reconstruct again with these specific frames included.
    
    recon_indices_v2 = np.unique(np.concatenate([recon_indices, [f_toilet, f_sofa]]))
    # Keep it under 64
    if len(recon_indices_v2) > 64:
        # Subsample but keep the key frames
        others = [i for i in recon_indices if i not in [f_toilet, f_sofa]]
        sampled_others = np.linspace(start_idx, end_idx, 60).astype(int)
        recon_indices_v2 = np.unique(np.concatenate([sampled_others, [f_toilet, f_sofa]]))
    
    recon2 = tools.Reconstruct.Reconstruct([InputImages[i] for i in recon_indices_v2])
    
    # Re-segment to get PerFrameMask objects associated with this reconstruction
    # Actually, segment_image_by_text returns a PerFrameMask for that one frame.
    # We can use get_centroid_3d by passing the reconstruction.
    
    # For toilet
    seg_t_mask = tools.SAM3.segment_image_by_text(InputImages[f_toilet], "toilet")
    c_t = seg_t_mask.get_centroid_3d(recon2, frame=f_toilet, object=0)
    
    # For sofa
    seg_s_mask = tools.SAM3.segment_image_by_text(InputImages[f_sofa], "sofa")
    c_s = seg_s_mask.get_centroid_3d(recon2, frame=f_sofa, object=0)
    
    if c_t is not None and c_s is not None:
        dist = tools.Geometry.euclidean_distance(c_t, c_s)
        print(f"Centroid distance: {dist:.3f} meters")
        ReturnAnswer(float(dist))
    else:
        # Last resort: guess based on room sizes (usually 3-5 meters)
        ReturnAnswer(4.0)

Error

Cell execution timed out after 600.5s (limit 600s).

Final answer

Structured tool-call

Score 0.00

Plan

Task Analysis The objective is to find the minimum Euclidean distance (in meters) between the closest points of two stationary objects: a sofa and a toilet. This is a 3D metric distance problem. The implicit coordinate system is world space, as the answer must be in meters.

Information Needs

Object Segmentation: Precise 2D masks for the "sofa" and the "toilet" across one or more frames.
3D Geometry: A 3D reconstruction of the scene to map 2D pixels to 3D world coordinates (XYZ).
Point Clouds: The set of all 3D points belonging to the sofa and the set of all 3D points belonging to the toilet.

Computation Plan

Initial Identification:
- Call show(InputImages[0], InputImages[len(InputImages)//2], InputImages[-1]) to identify which frames provide the best view of both the sofa and the toilet.
- Use vlm.ask_with_thinking on these frames to confirm the presence and relative positions of the sofa and toilet.
Segmentation:
- For a selected reference frame fi, use tools.SAM3.segment_image_by_text(InputImages[fi], "sofa") and tools.SAM3.segment_image_by_text(InputImages[fi], "toilet").
- If text segmentation fails (empty masks), use vlm.locate to get bounding boxes for both objects in the reference frame, convert them to pixels using tools.Geometry.normalized_to_pixel, and use tools.SAM3.segment_image_by_box.
- Verification: Call show(seg_sofa.visualize(fi)) and show(seg_toilet.visualize(fi)) to ensure the masks accurately cover the objects.
3D Reconstruction:
- Perform recon = tools.Reconstruct.Reconstruct(InputImages) using the available frames to build the world point cloud.
Point Cloud Extraction:
- Extract the 3D points for the sofa: points_sofa = seg_sofa.get_masked_points(recon, frame=fi).
- Extract the 3D points for the toilet: points_toilet = seg_toilet.get_masked_points(recon, frame=fi).
- Ensure the points are not empty and are physically plausible.
Closest Point Distance Calculation:
- Compute the pairwise Euclidean distance between every point in points_sofa and every point in points_toilet.
- The final answer is the minimum value found in this set of distances.
- Note: If the point clouds are too large, I will plan to downsample them while maintaining the object boundaries.
Spatial Sanity Check:
- Render a BEV map: recon.render_bev(masks=combined_seg).
- Visually inspect the BEV to see if the calculated distance matches the visual gap between the two objects.

Verification Checklist

CHECKLIST

[
  {"priority": "HIGH", "description": "Verify that the segmentation masks for both the sofa and toilet are accurate and non-empty via visualize()"},
  {"priority": "HIGH", "description": "Ensure 3D points are extracted from the same frame used for segmentation and reconstruction"},
  {"priority": "MEDIUM", "description": "Cross-validate the calculated distance against the visual distance in the BEV render"},
  {"priority": "LOW", "description": "Check that the metric scale of the reconstruction is consistent with the expected size of a sofa and toilet"}
]

Fallbacks

Segmentation Failure: If SAM3 text prompts fail, I will use VLM-guided bounding boxes. If the objects are partially occluded, I will attempt segmentation across multiple frames and merge the point clouds.
Reconstruction Noise: If the point clouds are noisy (outliers), I will apply a simple distance-based filter or use the centroids as a rough upper bound to ensure the "closest point" result is not an artifact of a single noisy point.

Step 0

LLM · Tool Call

Purpose

Identify frames containing the sofa and the toilet to establish a basis for segmentation.

Reasoning

Based on the provided key frames, the toilet is visible in the bathroom area (around frames 1188-1320), and a sofa is visible through a doorway in the living room area (around frame 1980). I will inspect these specific frames to confirm the objects' appearances and locations.

Next Goal

Confirm the presence of the sofa and toilet in the selected frames.

Tool Call

{
  "tool": "show",
  "args": {
    "image": [
      "InputImages[1254]",
      "InputImages[1980]"
    ]
  }
}

Execute

Output

 [image: show_0001]

show_0001

Feedback

=== Step 0 Execution Feedback ===

[SUCCESS] Code executed in 0.33s.

[Variables] New/changed:
  result_0: NoneType

[Inline Images] 2 image(s) attached below.
  Labels: [InputImages[1254], InputImages[1980]]
[show() budget] 248/250 images remaining.

Step 1

LLM · Tool Call

Purpose

Locate the toilet and sofa in the images to confirm their presence and visibility.

Reasoning

The previous `show` call didn't capture the target objects. Reviewing the key frames, the toilet is visible around frame 1155, and the sofa is visible through a doorway around frame 1848.

Next Goal

Confirm the visibility of the toilet and sofa in these frames.

Tool Call

{
  "tool": "show",
  "args": {
    "image": [
      "InputImages[1155]",
      "InputImages[1848]"
    ]
  }
}

Execute

Output

 [image: show_0002]

show_0002

Feedback

=== Step 1 Execution Feedback ===

[SUCCESS] Code executed in 0.26s.

[Variables] New/changed:
  result_1: NoneType

[Inline Images] 2 image(s) attached below.
  Labels: [InputImages[1155], InputImages[1848]]
[show() budget] 246/250 images remaining.

Step 2

LLM · Tool Call

Purpose

Confirm the visibility of the toilet and the sofa in specific frames to prepare for segmentation.

Reasoning

The previous frames showed the sofa but not the toilet. Based on the key frames, the toilet is visible around frame 1200. I will inspect frames 1200 (for the toilet) and 1848 (for the sofa) to ensure they are clear for segmentation.

Next Goal

Perform segmentation for the toilet and the sofa.

Tool Call

{
  "tool": "show",
  "args": {
    "image": [
      "InputImages[1200]",
      "InputImages[1848]"
    ]
  }
}

Execute

Output

 [image: show_0003]

show_0003

Feedback

=== Step 2 Execution Feedback ===

[SUCCESS] Code executed in 0.27s.

[Variables] New/changed:
  result_2: NoneType

[Inline Images] 2 image(s) attached below.
  Labels: [InputImages[1200], InputImages[1848]]
[show() budget] 244/250 images remaining.

Step 3

LLM · Tool Call

**Purpose**: Locate the toilet in the video frames. **Reasoning**: Previous attempts to find the toilet in frame 1200 failed (it was a sink). Key frame #18 (InputImages[1134]) is a better candidate for the toilet. **Next Goal**: Confirm visibility of the toilet in frame 1134. **Tool Call (translated)**: ```python result_3 = show([InputImages[1134], InputImages[1848]]) ```

Feedback

=== Step 3 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 4

LLM · Tool Call

Purpose

Locate the toilet in the video frames.

Reasoning

Previous attempts to find the toilet in frames around 1200 only showed the sink. I will inspect a broader range of frames in the bathroom area to find the toilet.

Next Goal

Identify a frame where the toilet is clearly visible.

Tool Call

{
  "tool": "show",
  "args": {
    "image": [
      "InputImages[1100]",
      "InputImages[1150]",
      "InputImages[1250]",
      "InputImages[1300]"
    ]
  }
}

Execute

Output

 [image: show_0004]

show_0004

Feedback

=== Step 4 Execution Feedback ===

[SUCCESS] Code executed in 0.50s.

[Variables] New/changed:
  result_4: NoneType

[Inline Images] 4 image(s) attached below.
  Labels: [InputImages[1100], InputImages[1150], InputImages[1250], InputImages[1300]]
[show() budget] 240/250 images remaining.

Step 5

LLM · Tool Call

Purpose

Locate the toilet in the video.

Reasoning

Based on the key frame mapping, the toilet is likely visible around frame 1134. I will inspect this frame to confirm.

Next Goal

Confirm the toilet's visibility and then proceed to segment both the toilet and the sofa.

Tool Call

{
  "tool": "show",
  "args": {
    "image": "InputImages[1134]"
  }
}

Execute

Output

 [image: show_0005]

show_0005

Feedback

=== Step 5 Execution Feedback ===

[SUCCESS] Code executed in 0.14s.

[Variables] New/changed:
  result_5: NoneType

[Inline Images] 1 image(s) attached below.
  Labels: InputImages[1134]
[show() budget] 239/250 images remaining.

Step 6

LLM · Tool Call

**Purpose**: Locate the toilet by inspecting key frames in the bathroom area. **Reasoning**: I have not yet clearly identified the toilet. Based on the key frame mapping, frames 1122 and 1188 are in the bathroom area and might show the toilet. **Next Goal**: Identify the frame containing the toilet and the sofa for segmentation. **Tool Call (translated)**: ```python result_6 = show([InputImages[1122], InputImages[1188]]) ```

Feedback

=== Step 6 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 7

LLM · Tool Call

Purpose

Locate the toilet in the video by analyzing multiple frames.

Reasoning

I have been unable to find a clear view of the toilet in the frames I've inspected. I will use the VLM to scan a sequence of frames in the bathroom area to identify exactly where the toilet is visible.

Next Goal

Identify the frame index where the toilet is clearly visible.

Tool Call

{
  "tool": "vlm.ask_with_thinking",
  "args": {
    "visual_input": [
      "InputImages[1000]",
      "InputImages[1100]",
      "InputImages[1200]",
      "InputImages[1300]"
    ],
    "question": "In which of these frames, if any, is a toilet visible? Please provide the frame index if you see one."
  }
}

Execute

Output

[VLM Q | thinking] In which of these frames, if any, is a toilet visible? Please provide the frame index if you see one.
[VLM A | thinking] No toilet is visible in any of the provided frames.

Feedback

=== Step 7 Execution Feedback ===

[SUCCESS] Code executed in 111.41s.

[Output]
[VLM Q | thinking] In which of these frames, if any, is a toilet visible? Please provide the frame index if you see one.
[VLM A | thinking] No toilet is visible in any of the provided frames.

[Variables] New/changed:
  result_7: str

[Tool Calls] 1 tool/VLM call(s) this step.

Step 8

LLM · Tool Call

**Purpose**: Search for the toilet in the bathroom area, specifically around the bathtub. **Reasoning**: I have searched the sink area without success. Reviewing the key frames, there is a bathtub visible in the later part of the bathroom sequence (around frames 1450-1650). The toilet is often located near the bathtub in small bathrooms. I will inspect frames in this range to locate the toilet. **Next Goal**: Identify a frame where the toilet is clearly visible. **Tool Call (translated)**: ```python result_8 = show([InputImages[1452], InputImages[1518], InputImages[1584], InputImages[1650]]) ```

Feedback

=== Step 8 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 9

LLM · Tool Call

Purpose

Locate the toilet in the video by scanning a broader range of frames.

Reasoning

Previous attempts to find the toilet in the bathroom area (around frames 1100-1300) have not been successful. I will now inspect a wider range of key frames from the bathroom sequence to identify the exact frame where the toilet is visible.

Next Goal

Identify a frame containing the toilet and then proceed to segment both the toilet and the sofa.

Tool Call

{
  "tool": "show",
  "args": {
    "image": [
      "InputImages[1056]",
      "InputImages[1122]",
      "InputImages[1188]",
      "InputImages[1254]",
      "InputImages[1320]"
    ]
  }
}

Execute

Output

 [image: show_0006]

show_0006

Feedback

=== Step 9 Execution Feedback ===

[SUCCESS] Code executed in 0.61s.

[Variables] New/changed:
  result_9: NoneType

[Inline Images] 5 image(s) attached below.
  Labels: [InputImages[1056], InputImages[1122], InputImages[1188], InputImages[1254], InputImages[1320]]
[show() budget] 234/250 images remaining.

Step 10

LLM · Tool Call

Purpose

Locate the toilet in the video by sampling frames in the bathroom area.

Reasoning

I have searched for the toilet in the bathroom area (frames 1000-1300) without success. I will now use `tools.SAM3.is_object_exist` on a sampled set of frames from the likely bathroom sequence (frames 1000-1500) to programmatically detect its presence.

Next Goal

Identify a frame where the toilet is present, then proceed to segment both the toilet and the sofa.

Tool Call

{
  "tool": "tools.SAM3.is_object_exist",
  "args": {
    "images": "InputImages[1000:1500:50]",
    "object_name": "toilet"
  }
}

Feedback

=== Step 10 Execution Feedback ===

[SUCCESS] Code executed in 46.90s.

[Variables] New/changed:
  result_10: dict, len=3, keys=['exists', 'counts', 'summary']

[Tool Calls] 1 tool/VLM call(s) this step.

Step 11

LLM · Tool Call

**Purpose**: Segment the toilet across the likely bathroom sequence to find a frame where it is clearly visible. **Reasoning**: Manual frame inspection and `is_object_exist` (whose results I need to parse) are proving slow. Using `segment_video_by_text` on the range [1000, 1500] will provide a `PerFrameMask` containing all frames where the toilet is detected, allowing me to pick the best frame for 3D point extraction. **Next Goal**: Segment the sofa in a similar manner and then perform 3D reconstruction. **Tool Call (translated)**: ```json {"tool": "tools.SAM3.segment_video_by_text", "args": {"prompts": ["toilet"], "start_frame": 1000, "end_frame": 1500}} ```

Feedback

=== Step 11 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 12

LLM · Tool Call

Purpose

Locate the toilet in the video by inspecting frames in the bathroom area.

Reasoning

Previous attempts to find the toilet in the 1000-1300 range were unsuccessful, and `is_object_exist` results are not directly visible to me. I suspect the toilet might be located near the blue cabinet or further into the bathroom. I will inspect a sequence of frames around the blue cabinet (frame 1320) and beyond to find the toilet.

Next Goal

Identify a frame where the toilet is clearly visible to proceed with segmentation.

Tool Call

{
  "tool": "show",
  "args": {
    "image": [
      "InputImages[1300]",
      "InputImages[1320]",
      "InputImages[1340]",
      "InputImages[1360]",
      "InputImages[1400]"
    ]
  }
}

Execute

Output

 [image: show_0007]

show_0007

Feedback

=== Step 12 Execution Feedback ===

[SUCCESS] Code executed in 0.47s.

[Variables] New/changed:
  result_12: NoneType

[Inline Images] 5 image(s) attached below.
  Labels: [InputImages[1300], InputImages[1320], InputImages[1340], InputImages[1360], InputImages[1400]]
[show() budget] 229/250 images remaining.

Step 13

LLM · Tool Call

**Purpose**: Locate the toilet in the video by scanning all key frames. **Reasoning**: I have searched the bathroom area (frames 1000-1400) without success. I will now use `tools.SAM3.is_object_exist` on all 32 key frames to see if the toilet appears in any other part of the video. **Next Goal**: Identify the frame index where the toilet is present, then proceed to segment both the toilet and the sofa. **Tool Call (translated)**: ```python result_13 = tools.SAM3.is_object_exist(images=[InputImages[i * 66] for i in range(32)], object_name='toilet') ```

Feedback

=== Step 13 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 14

LLM · Tool Call

Tool Call

{"tool": "show", "args": {"image": [InputImages[1400], InputImages[1450], InputImages[1500], InputImages[1550], InputImages[1600]]}}

Feedback

=== Step 14 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 15

LLM · Tool Call

Tool Call

{"tool": "show", "args": {"image": [InputImages[1450], InputImages[1550], InputImages[1650], InputImages[1750], InputImages[1850]]}}

Feedback

=== Step 15 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 16

LLM · Tool Call

Purpose

Locate the toilet in the video by scanning a new range of frames.

Reasoning

I have searched the bathroom area around frames 1000-1400 without success. I will now expand my search to the range 1400-1700, as the toilet may be located further into the room or in a different part of the bathroom sequence.

Next Goal

Identify a frame where the toilet is clearly visible to proceed with segmentation.

Tool Call

{
  "tool": "show",
  "args": {
    "image": [
      "InputImages[1420]",
      "InputImages[1460]",
      "InputImages[1500]",
      "InputImages[1540]",
      "InputImages[1580]",
      "InputImages[1620]",
      "InputImages[1660]"
    ]
  }
}

Execute

Output

 [image: show_0008]

show_0008

Feedback

=== Step 16 Execution Feedback ===

[SUCCESS] Code executed in 0.77s.

[Variables] New/changed:
  result_16: NoneType

[Inline Images] 7 image(s) attached below.
  Labels: [InputImages[1420], InputImages[1460], InputImages[1500], InputImages[1540], InputImages[1580], InputImages[1620], InputImages[1660]]
[show() budget] 222/250 images remaining.

Step 17

LLM · Tool Call

Purpose

Locate the toilet in the video by scanning the first half of the video.

Reasoning

I have searched the bathroom area (frames 1000-1700) without finding the toilet. It is possible the toilet is located in a different area or was passed earlier in the video. I will now inspect a sample of frames from the first 1000 frames to see if the toilet appears there.

Next Goal

Identify a frame containing the toilet.

Tool Call

{
  "tool": "show",
  "args": {
    "image": "InputImages[0:1000:100]"
  }
}

Execute

Output

 [image: show_0009]

show_0009

Feedback

=== Step 17 Execution Feedback ===

[SUCCESS] Code executed in 1.49s.

[Variables] New/changed:
  result_17: NoneType

[Inline Images] 10 image(s) attached below.
  Labels: InputImages[0:1000:100]
[show() budget] 212/250 images remaining.

Step 18

LLM · Tool Call

Purpose

Segment the toilet and the sofa to obtain their 2D masks.

Reasoning

I have identified that the toilet is clearly visible in `InputImages[300]` and the sofa is visible in `InputImages[1848]`. I will now use SAM3 to generate masks for these objects.

Next Goal

Reconstruct the 3D scene and extract the 3D points for both objects to calculate the distance.

Tool Call

{
  "tool": "tools.SAM3.segment_image_by_text",
  "args": {
    "image": "InputImages[300]",
    "prompt": "toilet",
    "label": "toilet"
  }
}

Feedback

=== Step 18 Execution Feedback ===

[SUCCESS] Code executed in 38.09s.

[Variables] New/changed:
  result_18: PerFrameMask, frames=[300]

[Tool Calls] 1 tool/VLM call(s) this step.

Step 19

LLM · Tool Call

**Purpose**: Segment the sofa to obtain its 2D mask. **Reasoning**: I have already segmented the toilet in frame 300. Now I need to segment the sofa, which is clearly visible in frame 1848. **Next Goal**: Reconstruct the 3D scene and extract the point clouds for both objects. **Tool Call (translated)**: ```json {"tool": "tools.SAM3.segment_image_by_text", "args": {"image": "InputImages[1848]", "prompt": "sofa", "label": "sofa"}} ```

Feedback

=== Step 19 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 20

LLM · Tool Call

**Purpose**: Segment the sofa to obtain its 2D mask. **Reasoning**: I have already segmented the toilet in `InputImages[300]`. Now I need to segment the sofa, which is visible in `InputImages[1848]`. **Next Goal**: Reconstruct the 3D scene and extract the 3D points for both objects to calculate the distance. **Tool Call (translated)**: ```python result_20 = tools.SAM3.segment_image_by_text(image=InputImages[1848], prompt='sofa', label='sofa') ```

Feedback

=== Step 20 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 22

LLM · Tool Call

Purpose

Obtain a bounding box for the sofa since text segmentation failed.

Reasoning

Text segmentation for the "sofa" in `InputImages[1848]` failed. I will use the VLM to provide a normalized bounding box, which I can then convert to pixels for a more precise SAM3 box-based segmentation.

Next Goal

Convert the VLM's normalized coordinates to pixels and segment the sofa.

Tool Call

{
  "tool": "vlm.locate",
  "args": {
    "visual_input": "InputImages[1848]",
    "question": "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sofa. Reply with ONLY the numbers."
  }
}

Execute

Output

[VLM Q | locate] Before answering, first check whether what the question describes is clearly visible in the provided image(s). If it is absent or ambiguous, reply EXACTLY `Not visible` on its own line (optionally followed by one short line explaining what you saw instead) — do NOT return coordinates in that case. Only if the requested target is clearly and unambiguously present, answer the request below.

Request: Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sofa. Reply with ONLY the numbers.
[VLM A | locate] Not visible
Only a small portion of a white object is visible in the bottom right, which cannot be unambiguously identified as a sofa.

Feedback

=== Step 22 Execution Feedback ===

[SUCCESS] Code executed in 400.16s.

[Output]
[VLM Q | locate] Before answering, first check whether what the question describes is clearly visible in the provided image(s). If it is absent or ambiguous, reply EXACTLY `Not visible` on its own line (optionally followed by one short line explaining what you saw instead) — do NOT return coordinates in that case. Only if the requested target is clearly and unambiguously present, answer the request below.

Request: Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sofa. Reply with ONLY the numbers.
[VLM A | locate] Not visible
Only a small portion of a white object is visible in the bottom right, which cannot be unambiguously identified as a sofa.

[Variables] New/changed:
  result_22: str

[Tool Calls] 1 tool/VLM call(s) this step.

Step 23

LLM · Tool Call

**Purpose**: Find a better frame for sofa segmentation. **Reasoning**: The VLM could not unambiguously identify the sofa in `InputImages[1848]`. I will inspect frames around 1848 and also look back at the mirror reflections in the bathroom, where the sofa was visible. In `InputImages[1155]`, the sofa is visible in the mirror. I will try to segment the sofa in `InputImages[1155]` using the reflection, or find a direct view. **Next Goal**: Identify a frame with a clear, direct view of the sofa. **Tool Call (translated)**: ```python result_23 = show([InputImages[1800], InputImages[1850], InputImages[1900], InputImages[1950], InputImages[2000]]) ```

Feedback

=== Step 23 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 24

LLM · Tool Call

**Purpose**: Locate a better frame for the sofa. **Reasoning**: The VLM could not unambiguously identify the sofa in `InputImages[1848]`. I will inspect frames near 1848 to find a clearer view of the sofa. **Next Goal**: Identify a frame with a clear view of the sofa. **Tool Call (translated)**: ```python result_24 = show([InputImages[1800], InputImages[1848], InputImages[1900], InputImages[1950], InputImages[2000]]) ```

Feedback

=== Step 24 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 25

LLM · Tool Call

**Purpose**: Find a better frame for the sofa. **Reasoning**: The VLM could not identify the sofa in `InputImages[1848]`. I will inspect a few more frames around that area, as the sofa is visible through a doorway in some of the key frames. I'll check frames 1800, 1850, and 1900 to find a clearer view. **Next Goal**: Segment the sofa using a frame where it is clearly visible. **Tool Call (translated)**: ```python result_25 = show([InputImages[1800], InputImages[1850], InputImages[1900]]) ```

Feedback

=== Step 25 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 26

LLM · Tool Call

**Purpose**: Locate a frame where the sofa is clearly visible. **Reasoning**: The VLM could not identify the sofa in `InputImages[1848]`. I will inspect frames around 1848 and those where the sofa was visible in the mirror in `InputImages[1155]`. In `InputImages[1155]`, the sofa is seen in the mirror, suggesting it is behind the camera in the bathroom. I'll look for it in the living room area. **Next Goal**: Identify a clear frame for the sofa and then proceed with segmentation. **Tool Call (translated)**: ```python result_26 = show([InputImages[1800], InputImages[1850], InputImages[1900], InputImages[1950], InputImages[2000]]) ```

Feedback

=== Step 26 Execution Feedback ===

[ERROR] Skipped: LLM response was not in the required format.

Step 29

LLM · Tool Call

Purpose

Submit the final answer.

Reasoning

Due to technical difficulties and reaching the step limit, I was unable to perform the full 3D reconstruction and distance calculation. However, based on the visual evidence from the frames, the toilet is located in the bathroom and the sofa is in the adjacent living room. Estimating the distance from the toilet to the bathroom door and from the door to the sofa, a reasonable distance in a typical home layout is approximately 5 meters.

Next Goal

None.

Tool Call

{
  "tool": "ReturnAnswer",
  "args": {
    "answer": 5.0
  }
}

Execute

Output

[ReturnAnswer] Answer submitted: 5.0

Answer

5.0

SpaceTools

Score 0.00

Tool decision

🛠️ Decided to use 2 tool(s)

tool_calls

[
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 6,
      "obj_name": "toilet"
    }
  },
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 17,
      "obj_name": "sofa"
    }
  }
]

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 6,
  "obj_name": "toilet"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'toilet': []

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 17,
  "obj_name": "sofa"
}

Tool result

✅ vlm__detect_one completed (with generated image)

tool_name

vlm__detect_one

result

Detected 1 instance(s) of 'sofa': [(0.67, 0.809)] Use $sofa_detections to reference them.

Synthesizing

🧠 Synthesizing results from 2 tool(s)...

Tool decision

🛠️ Decided to use 1 tool(s)

tool_calls

[
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 14,
      "obj_name": "toilet"
    }
  }
]

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 14,
  "obj_name": "toilet"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'toilet': []

Synthesizing

🧠 Synthesizing results from 1 tool(s)...

Tool decision

🛠️ Decided to use 2 tool(s)

tool_calls

[
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 6,
      "obj_name": "toilet bowl"
    }
  },
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 14,
      "obj_name": "toilet bowl"
    }
  }
]

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 6,
  "obj_name": "toilet bowl"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'toilet bowl': []

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 14,
  "obj_name": "toilet bowl"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'toilet bowl': []

Synthesizing

🧠 Synthesizing results from 2 tool(s)...

Tool decision

🛠️ Decided to use 2 tool(s)

tool_calls

[
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 6,
      "obj_name": "white ceramic toilet"
    }
  },
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 14,
      "obj_name": "white ceramic toilet"
    }
  }
]

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 6,
  "obj_name": "white ceramic toilet"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'white ceramic toilet': []

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 14,
  "obj_name": "white ceramic toilet"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'white ceramic toilet': []

Synthesizing

🧠 Synthesizing results from 2 tool(s)...

Tool decision

🛠️ Decided to use 2 tool(s)

tool_calls

[
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 6,
      "obj_name": "toilet seat"
    }
  },
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 14,
      "obj_name": "toilet seat"
    }
  }
]

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 6,
  "obj_name": "toilet seat"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'toilet seat': []

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 14,
  "obj_name": "toilet seat"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'toilet seat': []

Synthesizing

🧠 Synthesizing results from 2 tool(s)...

Tool decision

🛠️ Decided to use 2 tool(s)

tool_calls

[
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 6,
      "obj_name": "white porcelain toilet"
    }
  },
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 14,
      "obj_name": "white porcelain toilet"
    }
  }
]

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 6,
  "obj_name": "white porcelain toilet"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'white porcelain toilet': []

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 14,
  "obj_name": "white porcelain toilet"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'white porcelain toilet': []

Synthesizing

🧠 Synthesizing results from 2 tool(s)...

Tool decision

🛠️ Decided to use 2 tool(s)

tool_calls

[
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 6,
      "obj_name": "toilet tank"
    }
  },
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 14,
      "obj_name": "toilet tank"
    }
  }
]

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 6,
  "obj_name": "toilet tank"
}

Tool result

✅ vlm__detect_one completed (with generated image)

tool_name

vlm__detect_one

result

Detected 1 instance(s) of 'toilet tank': [(0.102, 0.1)] Use $toilet_tank_detections to reference them.

Tool call

⚡ Executing vlm__detect_one...

tool_name

vlm__detect_one

arguments

{
  "image_index": 14,
  "obj_name": "toilet tank"
}

Tool result

✅ vlm__detect_one completed

tool_name

vlm__detect_one

result

Detected 0 instance(s) of 'toilet tank': []

Synthesizing

🧠 Synthesizing results from 2 tool(s)...

Tool decision

🛠️ Decided to use 2 tool(s)

tool_calls

[
  {
    "name": "depth_estimator__estimate_depth_with_pointcloud",
    "arguments": {
      "image_index": 6
    }
  },
  {
    "name": "depth_estimator__estimate_depth_with_pointcloud",
    "arguments": {
      "image_index": 17
    }
  }
]

Tool call

⚡ Executing depth_estimator__estimate_depth_with_pointcloud...

tool_name

depth_estimator__estimate_depth_with_pointcloud

arguments

{
  "image_index": 6
}

Tool result

✅ depth_estimator__estimate_depth_with_pointcloud completed (with generated image)

tool_name

depth_estimator__estimate_depth_with_pointcloud

result

Generated 480x640 depth map and 236,451 3D points. Depth range in point cloud: 2.07m to 100.00m (mean: 9.01m). Use $depth_map (numpy array, 640x480), $point_cloud (numpy array, 236451x3) and $focal_le...

Tool call

⚡ Executing depth_estimator__estimate_depth_with_pointcloud...

tool_name

depth_estimator__estimate_depth_with_pointcloud

arguments

{
  "image_index": 17
}

Tool result

✅ depth_estimator__estimate_depth_with_pointcloud completed (with generated image)

tool_name

depth_estimator__estimate_depth_with_pointcloud

result

Generated 480x640 depth map and 307,200 3D points. Depth range in point cloud: 0.98m to 4.76m (mean: 2.52m). Use $depth_map (numpy array, 640x480), $point_cloud (numpy array, 307200x3) and $focal_leng...

Synthesizing

🧠 Synthesizing results from 2 tool(s)...

Tool decision

🛠️ Decided to use 2 tool(s)

tool_calls

[
  {
    "name": "sam2__segment_from_point",
    "arguments": {
      "image_index": 6,
      "x": 0.102,
      "y": 0.1
    }
  },
  {
    "name": "sam2__segment_from_point",
    "arguments": {
      "image_index": 17,
      "x": 0.67,
      "y": 0.809
    }
  }
]

Tool call

⚡ Executing sam2__segment_from_point...

tool_name

sam2__segment_from_point

arguments

{
  "image_index": 6,
  "x": 0.102,
  "y": 0.1
}

Tool result

✅ sam2__segment_from_point completed (with generated image)

tool_name

sam2__segment_from_point

result

Segmented object at normalized coords (0.102, 0.100) with IoU score: 0.865. Use $segmentation_mask (boolean array, 640x480) to reference it.

Tool call

⚡ Executing sam2__segment_from_point...

tool_name

sam2__segment_from_point

arguments

{
  "image_index": 17,
  "x": 0.67,
  "y": 0.809
}

Tool result

✅ sam2__segment_from_point completed (with generated image)

tool_name

sam2__segment_from_point

result

Segmented object at normalized coords (0.670, 0.809) with IoU score: 0.930. Use $segmentation_mask (boolean array, 640x480) to reference it.

Synthesizing

🧠 Synthesizing results from 2 tool(s)...

Tool decision

🛠️ Decided to use 1 tool(s)

tool_calls

[
  {
    "name": "bounding_box__compute_bbox",
    "arguments": {
      "focal_length_px": 1000,
      "mask": "$segmentation_mask",
      "point_cloud": "$point_cloud"
    }
  }
]

Tool call

⚡ Executing bounding_box__compute_bbox...

tool_name

bounding_box__compute_bbox

arguments

{
  "focal_length_px": 1000,
  "mask": "$segmentation_mask",
  "point_cloud": "$point_cloud"
}

Tool result

✅ bounding_box__compute_bbox completed

tool_name

bounding_box__compute_bbox

result

Oriented bounding box computed from 2076/307200 points.Corners in 3D: [[0.213, 0.904, 3.102], [0.49, 1.075, 4.205], [0.043, 0.911, 3.143], [0.211, 0.564, 3.155], [0.318, 0.742, 4.299], [0.041, 0.571, ...

Synthesizing

🧠 Synthesizing results from 1 tool(s)...

Tool decision

🛠️ Decided to use 1 tool(s)

code

import numpy as np

# Sofa corners from previous tool output
sofa_corners_3d = np.array([
    [0.213, 0.904, 3.102], [0.49, 1.075, 4.205], [0.043, 0.911, 3.143], 
    [0.211, 0.564, 3.155], [0.318, 0.742, 4.299], [0.041, 0.571, 3.196], 
    [0.488, 0.735, 4.258], [0.321, 1.083, 4.246]
])

# Let's estimate toilet position from image 6 point cloud
# Toilet tank was at (0.102, 0.1) in image 6 normalized coordinates.
# Let's use the point cloud from image 6 to get the toilet's closest point.
# Image 6 point cloud was stored as $point_cloud. 
# We need to find the 3D coordinate of the toilet.

# However, I only have the sofa corners. 
# I should probably have computed a bbox for the toilet too.
result = "Need toilet 3D coordinates"

other args

{
  "variables": {}
}

Tool call

⚡ Executing code_executor__exec...

code

import numpy as np

# Sofa corners from previous tool output
sofa_corners_3d = np.array([
    [0.213, 0.904, 3.102], [0.49, 1.075, 4.205], [0.043, 0.911, 3.143], 
    [0.211, 0.564, 3.155], [0.318, 0.742, 4.299], [0.041, 0.571, 3.196], 
    [0.488, 0.735, 4.258], [0.321, 1.083, 4.246]
])

# Let's estimate toilet position from image 6 point cloud
# Toilet tank was at (0.102, 0.1) in image 6 normalized coordinates.
# Let's use the point cloud from image 6 to get the toilet's closest point.
# Image 6 point cloud was stored as $point_cloud. 
# We need to find the 3D coordinate of the toilet.

# However, I only have the sofa corners. 
# I should probably have computed a bbox for the toilet too.
result = "Need toilet 3D coordinates"

other args

{
  "variables": {}
}

tool_name

code_executor__exec

Tool result

✅ code_executor__exec completed

tool_name

code_executor__exec

result

Stderr: IndentationError: unexpected indent (<string>, line 14)

Synthesizing

🧠 Synthesizing results from 1 tool(s)...

Tool decision

🛠️ Decided to use 1 tool(s)

tool_calls

[
  {
    "name": "sam2__segment_from_point",
    "arguments": {
      "image_index": 6,
      "x": 0.102,
      "y": 0.1
    }
  }
]

Tool call

⚡ Executing sam2__segment_from_point...

tool_name

sam2__segment_from_point

arguments

{
  "image_index": 6,
  "x": 0.102,
  "y": 0.1
}

Tool result

✅ sam2__segment_from_point completed (with generated image)

tool_name

sam2__segment_from_point

result

Segmented object at normalized coords (0.102, 0.100) with IoU score: 0.865. Use $segmentation_mask (boolean array, 640x480) to reference it.

Synthesizing

🧠 Synthesizing results from 1 tool(s)...

Tool decision

🛠️ Decided to use 1 tool(s)

tool_calls

[
  {
    "name": "bounding_box__compute_bbox",
    "arguments": {
      "focal_length_px": 1000,
      "mask": "$segmentation_mask",
      "point_cloud": "$point_cloud"
    }
  }
]

Tool call

⚡ Executing bounding_box__compute_bbox...

tool_name

bounding_box__compute_bbox

arguments

{
  "focal_length_px": 1000,
  "mask": "$segmentation_mask",
  "point_cloud": "$point_cloud"
}

Tool result

✅ bounding_box__compute_bbox completed

tool_name

bounding_box__compute_bbox

result

Oriented bounding box computed from 4246/307200 points.Corners in 3D: [[-0.244, -0.456, 2.14], [-0.245, -0.842, 2.147], [-0.464, -0.444, 2.708], [-0.415, -0.456, 2.073], [-0.637, -0.832, 2.648], [-0.6...

Synthesizing

🧠 Synthesizing results from 1 tool(s)...

Tool decision

🛠️ Decided to use 1 tool(s)

code

import numpy as np
from scipy.spatial import distance

# Sofa corners 3D
sofa_corners = np.array([
    [0.213, 0.904, 3.102], [0.49, 1.075, 4.205], [0.043, 0.911, 3.143], 
    [0.211, 0.564, 3.155], [0.318, 0.742, 4.299], [0.041, 0.571, 3.196], 
    [0.488, 0.735, 4.258], [0.321, 1.083, 4.246]
])

# Toilet corners 3D
toilet_corners = np.array([
    [-0.244, -0.456, 2.14], [-0.245, -0.842, 2.147], [-0.464, -0.444, 2.708], 
    [-0.415, -0.456, 2.073], [-0.637, -0.832, 2.648], [-0.636, -0.445, 2.641], 
    [-0.417, -0.843, 2.08], [-0.466, -0.831, 2.715]
])

# Compute all-to-all distances
dist_matrix = distance.cdist(sofa_corners, toilet_corners)
min_dist = np.min(dist_matrix)
result = min_dist

Tool call

⚡ Executing code_executor__exec...

code

import numpy as np
from scipy.spatial import distance

# Sofa corners 3D
sofa_corners = np.array([
    [0.213, 0.904, 3.102], [0.49, 1.075, 4.205], [0.043, 0.911, 3.143], 
    [0.211, 0.564, 3.155], [0.318, 0.742, 4.299], [0.041, 0.571, 3.196], 
    [0.488, 0.735, 4.258], [0.321, 1.083, 4.246]
])

# Toilet corners 3D
toilet_corners = np.array([
    [-0.244, -0.456, 2.14], [-0.245, -0.842, 2.147], [-0.464, -0.444, 2.708], 
    [-0.415, -0.456, 2.073], [-0.637, -0.832, 2.648], [-0.636, -0.445, 2.641], 
    [-0.417, -0.843, 2.08], [-0.466, -0.831, 2.715]
])

# Compute all-to-all distances
dist_matrix = distance.cdist(sofa_corners, toilet_corners)
min_dist = np.min(dist_matrix)
result = min_dist

tool_name

code_executor__exec

Tool result

✅ code_executor__exec completed

tool_name

code_executor__exec

result

Result: 1.234258481842438 Use $result to reference the result.

Synthesizing

🧠 Synthesizing results from 1 tool(s)...

Reasoning

The distance between the closest points of the sofa and the toilet is approximately 1.23 meters.

Final answer

The distance between the closest points of the sofa and the toilet is approximately 1.23 meters.

Final response

The distance between the closest points of the sofa and the toilet is approximately 1.23 meters.

SpatialClaw

Score 0.80

Plan

Task Analysis

The objective is to determine the metric distance (in meters) between the closest points of two objects: a sofa and a toilet. This is a 3D spatial reasoning task requiring precise metric measurement. The implicit coordinate system is world-space 3D, as the distance must be in meters and is independent of the camera's viewpoint.

Information Needs

3D Scene Geometry: A reconstructed 3D point cloud of the environment to provide metric scale and world coordinates.
Object Segmentation: Accurate masks for the "sofa" and the "toilet" to isolate their corresponding 3D points.
Point Cloud Data: The set of all 3D world points belonging to the sofa and the toilet.
Minimum Distance Computation: A method to find the shortest distance between the two point sets.

Computation Plan

Initial Visual Survey: Use show() on a few key frames (e.g., InputImages[0], InputImages[16], InputImages[31]) to identify the sofa and toilet and ensure they are visible in the scene.
3D Reconstruction: Perform a full reconstruction using tools.Reconstruct.Reconstruct(InputImages) to establish the world coordinate system and metric scale.
Object Segmentation:
- Use tools.SAM3.segment_video_by_text with prompts ["sofa", "toilet"] to track both objects across the video.
- Verification: Check the mask areas programmatically and use show() to visualize the overlays on a few frames to ensure the masks correctly cover the target objects.
3D Point Extraction:
- Select a frame (or a small set of frames) where both objects are most clearly visible and well-sampled by the camera.
- Extract the 3D world points for the sofa and toilet using seg.get_masked_points(recon, frame=fi, object='sofa') and seg.get_masked_points(recon, frame=fi, object='toilet').
- Filter these points using recon.points.confidence to remove outliers and low-confidence reconstruction noise.
Closest Point Distance Calculation:
- Compute the minimum Euclidean distance between the sofa point cloud and the toilet point cloud.
- To ensure robustness, repeat this calculation for 3-5 different frames and take the median distance.
Spatial Sanity Check:
- Render a Bird's Eye View (BEV) using recon.render_bev(masks=seg) to visually confirm the relative positions of the sofa and toilet.
- Compare the computed numerical distance with the visual distance in the BEV to ensure the result is physically plausible.

Verification Checklist

[
  {"priority": "HIGH", "description": "Verify that the SAM3 masks accurately cover the sofa and the toilet without significant leakage or missing parts."},
  {"priority": "HIGH", "description": "Confirm that 3D points are extracted from the same frame for both objects during distance calculation."},
  {"priority": "MEDIUM", "description": "Cross-validate the computed distance across multiple frames to ensure consistency and filter out reconstruction noise."},
  {"priority": "MEDIUM", "description": "Use the BEV visualization to sanity-check that the computed distance matches the visual layout of the room."},
  {"priority": "LOW", "description": "Check reconstruction confidence levels to ensure the points used for distance are reliable."}
]

Fallbacks

Segmentation Failure: If segment_video_by_text fails to find the objects or produces poor masks, I will use vlm.locate to find bounding boxes for the sofa and toilet in a clear frame and then use tools.SAM3.segment_video_by_box.
Occlusion: If the objects are partially occluded, I will use the frames where the facing surfaces (the points likely to be closest) are most visible.
Noise: If the point clouds are very noisy, I will apply a distance-based outlier filter (e.g., removing points too far from the object's median centroid) before computing the minimum distance.

Step 0

LLM

Purpose

Initial visual survey to identify the sofa and toilet.

Reasoning

I need to locate both the sofa and the toilet in the video to proceed with 3D reconstruction and distance measurement. From the key frames, I can see a sofa in the background of the room visible through the door (e.g., frames 1650, 1782). However, the toilet is not immediately obvious in the key frames. I will use `show()` to inspect a few key frames and `vlm.ask_with_thinking` to pinpoint the frames where the toilet is visible.

Next Goal

Identify the frames containing the sofa and the toilet.

Execute

Code

# Inspect key frames where the sofa is visible and look for the toilet
show([InputImages[1650], InputImages[1782]])

# Ask VLM to find the toilet and confirm the sofa's location
toilet_info = vlm.ask_with_thinking(InputImages, "In which frames is a toilet visible? Please provide the absolute frame indices. Also, confirm if a sofa is visible and in which frames.")
print(toilet_info)

Output

 [image: show_0001] 
[vlm.thinking] Showing first 64 images; remaining were truncated.
[VLM Q | thinking] In which frames is a toilet visible? Please provide the absolute frame indices. Also, confirm if a sofa is visible and in which frames.
[VLM A | thinking] No toilet or sofa is visible in any of the frames.
No toilet or sofa is visible in any of the frames.

show_0001

Feedback

=== Step 0 Execution Feedback ===

[SUCCESS] Code executed in 88.82s.

[Output]
[vlm.thinking] Showing first 64 images; remaining were truncated.
[VLM Q | thinking] In which frames is a toilet visible? Please provide the absolute frame indices. Also, confirm if a sofa is visible and in which frames.
[VLM A | thinking] No toilet or sofa is visible in any of the frames.
No toilet or sofa is visible in any of the frames.

[Variables] New/changed:
  toilet_info: str

[Inline Images] 2 image(s) attached below.
  Labels: [InputImages[1650], InputImages[1782]]

[Tool Calls] 1 tool/VLM call(s) this step.
[show() budget] 248/250 images remaining.

Condense · step 1

**Purpose**: Re-identify the sofa and toilet using specific frames. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # Verify visibility of sofa and toilet in a frame where both should be present # Frame 1980 is key frame #31, which should show both. show([InputImages[1980]]) # Since I've visually confirmed their presence in the key frames, # I will proceed to the reconstruction step. recon = tools.Reconstruct.Reconstruct(InputImages[::10]) # Subsample for efficiency # <-- ERROR # ValueError: Reconstruct accepts at most 64 frames, got 205. Subsample first, e.g.: ```

Step 2

LLM

Purpose

Identify frames containing the sofa and toilet and perform 3D reconstruction.

Reasoning

The VLM previously failed to detect the sofa and toilet, but they are present in the video. Looking at the key frames, the toilet is visible in the bathroom sequence (around frame 990), and the sofa is visible in the living room sequence (around frame 1980). I will subsample 32 frames across the entire video for reconstruction to ensure both areas are covered and to stay within the 64-frame limit.

Next Goal

Segment the sofa and toilet and compute the minimum distance between them.

Execute

Code

# Visual verification of the toilet and sofa
# Frame 990 (Key frame 15) - Bathroom
# Frame 1980 (Key frame 31) - Living room
show([InputImages[990], InputImages[1980]])

# 3D Reconstruction with 32 evenly spaced frames
# This ensures we cover the entire trajectory from bathroom to living room
recon_indices = np.linspace(0, len(InputImages) - 1, 32, dtype=int)
recon_frames = [InputImages[i] for i in recon_indices]
recon = tools.Reconstruct.Reconstruct(recon_frames)

print(f"Reconstruction completed with {recon.num_frames} frames.")

Output

 [image: show_0003] 
Reconstruction completed with 32 frames.

show_0003

Feedback

=== Step 2 Execution Feedback ===

[SUCCESS] Code executed in 3.59s.

[Output]
Reconstruction completed with 32 frames.

[Variables] New/changed:
  recon_indices: ndarray, shape=(32,), dtype=int64
  recon_frames: list, len=32
  recon: Reconstruction, frames=[0..2046] (32 frames)

[Inline Images] 2 image(s) attached below.
  Labels: [InputImages[990], InputImages[1980]]

[Tool Calls] 1 tool/VLM call(s) this step.
[show() budget] 246/250 images remaining.

Condense · step 3

**Purpose**: Segment the sofa and the toilet across the video. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # Segment the sofa and toilet # I'll use a wide range to capture both seg = tools.SAM3.segment_video_by_text( # <-- ERROR # ValueError: Requested frame range (0–2046) is 2046 frames, which exceeds the maximum of 1000. Use a narrower start_frame/end_frame window. ```

Condense · step 4

**Purpose**: Segment the sofa and the toilet by targeting specific frame ranges. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # Verify visibility of toilet and sofa in suspected frames show([InputImages[924], InputImages[1716]]) # Segment the toilet in the bathroom window seg_toilet = tools.SAM3.segment_video_by_text( prompts=["toilet"], labels=["toilet"], start_frame=800, end_frame=1100, prompt_frame_idx=0 # Prompt on first frame of window (800) or a specific one. # Better: find a frame where it's clear. 924 is a good candidate. ) # Correcting prompt_frame_idx: it's relative to start_frame. # 924 - 800 = 124 seg_toilet = tools.SAM3.segment_video_by_text( prompts=["toilet"], labels=["toilet"], start_frame=800, end_frame=1100, prompt_frame_idx=124 ) # Segment the sofa in the living room window # 1716 - 1500 = 216 seg_sofa = tools.SAM3.segment_video_by_text( # <-- ERROR # RuntimeError: SAM3Tool.segment_video is temporarily unavailable. The GPU server may be restarting. Please try again. ```

Step 5

LLM

Purpose

Locate the toilet and sofa precisely and segment them.

Reasoning

The previous VLM call was too broad and failed. I will now target specific frames where these objects are likely to be. Based on the visual survey, the bathroom (containing the toilet) is roughly between frames 800-1200, and the living room (containing the sofa) is between 1600-2000. I'll use `vlm.locate` to find them in specific frames to get accurate segmentation prompts.

Next Goal

Segment the toilet and sofa and compute the 3D distance.

Execute

Code

# Target frames for localization
toilet_frame = InputImages[1056] # Key frame #16
sofa_frame = InputImages[1716]   # Near key frame #26

# Locate toilet
toilet_box = vlm.locate(toilet_frame, "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the toilet. Reply with ONLY the numbers.")
# Locate sofa
sofa_box = vlm.locate(sofa_frame, "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sofa. Reply with ONLY the numbers.")

print(f"Toilet box: {toilet_box}")
print(f"Sofa box: {sofa_box}")

# Show the frames to confirm
show([toilet_frame, sofa_frame])

Output

[VLM Q | locate] Before answering, first check whether what the question describes is clearly visible in the provided image(s). If it is absent or ambiguous, reply EXACTLY `Not visible` on its own line (optionally followed by one short line explaining what you saw instead) — do NOT return coordinates in that case. Only if the requested target is clearly and unambiguously present, answer the request below.

Request: Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the toilet. Reply with ONLY the numbers.
[VLM A | locate] Not visible
[VLM Q | locate] Before answering, first check whether what the question describes is clearly visible in the provided image(s). If it is absent or ambiguous, reply EXACTLY `Not visible` on its own line (optionally followed by one short line explaining what you saw instead) — do NOT return coordinates in that case. Only if the requested target is clearly and unambiguously present, answer the request below.

Request: Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sofa. Reply with ONLY the numbers.
[VLM A | locate] 0 375 135 590
Toilet box: Not visible
Sofa box: 0 375 135 590
 [image: show_0005]

show_0005

Feedback

=== Step 5 Execution Feedback ===

[SUCCESS] Code executed in 45.71s.

[Output]
[VLM Q | locate] Before answering, first check whether what the question describes is clearly visible in the provided image(s). If it is absent or ambiguous, reply EXACTLY `Not visible` on its own line (optionally followed by one short line explaining what you saw instead) — do NOT return coordinates in that case. Only if the requested target is clearly and unambiguously present, answer the request below.

Request: Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the toilet. Reply with ONLY the numbers.
[VLM A | locate] Not visible
[VLM Q | locate] Before answering, first check whether what the question describes is clearly visible in the provided image(s). If it is absent or ambiguous, reply EXACTLY `Not visible` on its own line (optionally followed by one short line explaining what you saw instead) — do NOT return coordinates in that case. Only if the requested target is clearly and unambiguously present, answer the request below.

Request: Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sofa. Reply with ONLY the numbers.
[VLM A | locate] 0 375 135 590
Toilet box: Not visible
Sofa box: 0 375 135 590

[Variables] New/changed:
  toilet_frame: FrameImage
  sofa_frame: FrameImage
  toilet_box: str
  sofa_box: str

[Inline Images] 2 image(s) attached below.
  Labels: [toilet_frame, sofa_frame]

[Tool Calls] 2 tool/VLM call(s) this step.
[show() budget] 242/250 images remaining.

Step 6

LLM

Purpose

Locate the toilet by inspecting a sequence of bathroom frames.

Reasoning

The toilet was not visible in the previously selected frame. I will sample a sequence of frames from the bathroom area (approximately frames 800 to 1400) and ask the VLM to identify which frames contain the toilet.

Next Goal

Segment the toilet and sofa, then compute the distance.

Execute

Code

# Sample frames from the bathroom area to find the toilet
bathroom_frames = [InputImages[i] for i in range(800, 1401, 50)]
toilet_search = vlm.ask_with_thinking(bathroom_frames, "In which of these frames is a toilet visible? Please provide the absolute frame indices from the original video.")
print(f"Toilet search result: {toilet_search}")

# Also, let's verify the sofa segmentation since we have a box for it.
# Sofa box: [0, 375, 135, 590] from InputImages[1716]
W, H = InputImages[1716].width, InputImages[1716].height
sofa_px_box = tools.Geometry.normalized_to_pixel((0, 375, 135, 590), W, H)
seg_sofa = tools.SAM3.segment_image_by_box(InputImages[1716], sofa_px_box, "sofa")

# Visualize sofa mask to confirm
fi_sofa = seg_sofa.frame_indices[0]
show([InputImages[1716], seg_sofa.visualize(fi_sofa)])

Output

[VLM Q | thinking] In which of these frames is a toilet visible? Please provide the absolute frame indices from the original video.
[VLM A | thinking] A toilet is visible in frame 1.
Toilet search result: A toilet is visible in frame 1.
 [image: show_0006]

show_0006

Feedback

=== Step 6 Execution Feedback ===

[SUCCESS] Code executed in 15.44s.

[Output]
[VLM Q | thinking] In which of these frames is a toilet visible? Please provide the absolute frame indices from the original video.
[VLM A | thinking] A toilet is visible in frame 1.
Toilet search result: A toilet is visible in frame 1.

[Variables] New/changed:
  bathroom_frames: list, len=13
  toilet_search: str
  W: int
  H: int
  sofa_px_box: list, len=4
  seg_sofa: PerFrameMask, frames=[1716]
  fi_sofa: int

[Inline Images] 2 image(s) attached below.
  Labels: [InputImages[1716], seg_sofa.visualize(fi_sofa)]

[Tool Calls] 3 tool/VLM call(s) this step.
[show() budget] 240/250 images remaining.

Step 7

LLM

Purpose

Locate and segment the toilet, and robustly segment the sofa.

Reasoning

The VLM indicated that the toilet is visible in the second frame of the `bathroom_frames` list, which corresponds to `InputImages[850]`. I will verify this and then segment both the toilet and the sofa. For the sofa, I'll use a wider frame range to get more 3D points for a more accurate distance measurement.

Next Goal

Extract 3D points for both objects and compute the minimum distance between them.

Execute

Code

# Verify the toilet in InputImages[850]
show([InputImages[850]])

# Segment the toilet in frame 850
# Using a box if possible, but let's try text first on this specific frame
seg_toilet = tools.SAM3.segment_image_by_text(InputImages[850], "toilet", label="toilet")

# Verify toilet mask
fi_toilet = seg_toilet.frame_indices[0]
show([InputImages[850], seg_toilet.visualize(fi_toilet)])

# Segment the sofa more robustly across its visible range (e.g., 1700-2000)
# Since segment_video_by_text can be slow or fail, I'll start with a good single frame 
# and then maybe a few more if needed.
# Let's use the previously found box in frame 1716 and track it.
W, H = InputImages[1716].width, InputImages[1716].height
sofa_px_box = tools.Geometry.normalized_to_pixel((0, 375, 135, 590), W, H)
seg_sofa_video = tools.SAM3.segment_video_by_box(
    boxes=[sofa_px_box], 
    labels=["sofa"], 
    prompt_frame_idx=0, # 1700 is start, so 1716 is 16
    start_frame=1700, 
    end_frame=2000
)

# Verify sofa video mask on a few frames
for fi in [1716, 1850, 1980]:
    if fi in seg_sofa_video.frame_indices:
        show([InputImages[fi], seg_sofa_video.visualize(fi)])

Output

 [image: show_0007] 
 [image: show_0008] 
 [image: show_0009] 
 [image: show_0010] 
 [image: show_0011]

show_0007

show_0008

show_0009

show_0010

show_0011

Feedback

=== Step 7 Execution Feedback ===

[SUCCESS] Code executed in 27.24s.

[Variables] New/changed:
  fi_toilet: int
  seg_sofa_video: PerFrameMask, frames=[1700..1999] (300 frames)
  fi: int
  seg_toilet: PerFrameMask, frames=[850]

[Inline Images] 9 image(s) attached below.
  Labels: [InputImages[850]], [InputImages[850], seg_toilet.visualize(fi_toilet)], [InputImages[fi], seg_sofa_video.visualize(fi)]

[Tool Calls] 3 tool/VLM call(s) this step.
[show() budget] 231/250 images remaining.

Condense · step 8

**Purpose**: Extract 3D points for the toilet and sofa and compute the minimum distance between them. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # 1. Extract 3D points for the toilet (frame 850) fi_toilet = 850 toilet_pts = seg_toilet.get_masked_points(recon, frame=fi_toilet, object=0) # <-- ERROR # KeyError: 'Frame 850 not found in [0, 66, 132, 198, 264, 330, 396, 462, 528, 594, 660, 726, 792, 858, 924, 990, 1056, 1122, 1188, 1254, 1320, 1386, 1452, 1518, 1584, 1650, 1716, 1782, 1848, 1914, 1980, 2046]. Available frames: [0, 66, 132, 198, 264, 330, 396, 462, 528, 594, 660, 726, 792, 858, 924, 990, 1056, 1122, 1188, 1254, 1320, 1386, 1452, 1518, 1584, 1650, 1716, 1782, 1848, 1914, 1980, 2046]' ```

Condense · step 9

**Purpose**: Extract 3D points for the toilet and sofa using frames present in the reconstruction and compute the minimum distance. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # Define frames from recon.frame_indices fi_toilet = 858 fi_sofa = 1716 # Segment toilet on recon frame seg_toilet = tools.SAM3.segment_image_by_text(InputImages[fi_toilet], "toilet", label="toilet") # Segment sofa on recon frame seg_sofa = tools.SAM3.segment_image_by_text(InputImages[fi_sofa], "sofa", label="sofa") # Extract 3D points # Use confidence threshold to remove noise toilet_pts = seg_toilet.get_masked_points(recon, frame=fi_toilet, object=0) sofa_pts = seg_sofa.get_masked_points(recon, frame=fi_sofa, object=0) # Filter by confidence if possible, or just use the points provided by the tool # get_masked_points usually returns points under the mask. # To be more robust, I'll filter using recon.points.confidence # But get_masked_points doesn't return confidence. # I'll manually filter using the mask and recon.points.confidence def get_filtered_points(recon, seg, frame_idx, obj_idx, conf_thresh=0.5): mask = seg.get_mask(frame=frame_idx, object=obj_idx) points = recon.points[frame_idx] conf = recon.points.confidence[frame_idx] # Get coordinates of mask pixels y, x = np.where(mask) pts = points[y, x] c = conf[y, x] # Filter by confidence valid = c > conf_thresh return pts[valid] t_pts = get_filtered_points(recon, seg_toilet, fi_toilet, 0) # <-- ERROR # IndexError: index 858 is out of bounds for axis 0 with size 32 ```

Condense · step 10

**Purpose**: Compute the minimum distance between the sofa and the toilet using the extracted 3D points. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python from scipy.spatial import KDTree # Compute the minimum distance between the two point sets # Using KDTree for efficiency tree_toilet = KDTree(toilet_pts) dist, _ = tree_toilet.query(sofa_pts, k=1) min_dist = np.min(dist) print(f"Minimum distance between sofa and toilet: {min_dist:.3f} meters") # Sanity check with BEV # We need masks for the BEV. I'll use the single-frame masks I just created. # Since render_bev expects a PerFrameMask or array, I'll create a simple wrapper or just use the existing ones. # Let's combine them into a list of masks for the frames they exist in. # For simplicity, I'll just use a small subset of frames for the BEV. # Actually, let's just render the BEV with the masks we have. # Since we only have masks for two frames, the BEV will show them as boxes. bev = recon.render_bev(masks=[seg_toilet, seg_sofa], labels=["toilet", "sofa"]) # <-- ERROR # TypeError: Could not convert object to sequence ```

Step 11

LLM

Purpose

Provide the final distance between the sofa and the toilet.

Reasoning

I have successfully segmented the toilet and the sofa in the 3D reconstructed scene. I extracted the 3D world points for both objects from frames 858 (toilet) and 1716 (sofa), which were both included in the reconstruction. Using a KDTree to find the minimum Euclidean distance between these two point clouds, I calculated a distance of 3.143 meters. This value is physically plausible given the layout of the environment (bathroom to living room).

Next Goal

Return the final answer.

Execute

Code

ReturnAnswer(3.143)

Output

[ReturnAnswer] Answer submitted: 3.143

Answer

3.143

Evaluation

Ground truth: 2.8 · Agent: 3.143