VSI-Bench-U — Gemma4-31B — sample 1418

# Visual survey: show a selection of frames to understand the layout
# Frame 0: Entry/Bed area
# Frame 560: TV/Wardrobe area
# Frame 2580: Fireplace area
# Frame 4550: Bed/Table area
show([InputImages[0], InputImages[560], InputImages[2580], InputImages[4550]])

 [image: show_0001] 

# Ask the VLM to identify frames where each object is clearly visible
# I'll provide a set of key frames to help it locate them.
frames_for_vlm = [InputImages[0], InputImages[560], InputImages[1120], InputImages[1680], 
                  InputImages[2240], InputImages[2800], InputImages[3360], InputImages[3920], 
                  InputImages[4480], InputImages[5040], InputImages[5600]]

question = "I need to find the following objects in this video: a TV, a table (including bedside tables), a fireplace, a bed, and a chair. For each object, please tell me if it is present and describe which frames (or approximate time/index) it appears in most clearly. If any object is missing, please state so."

answer = vlm.ask_with_thinking(frames_for_vlm, question)
print(answer)

[VLM Q | thinking] I need to find the following objects in this video: a TV, a table (including bedside tables), a fireplace, a bed, and a chair. For each object, please tell me if it is present and describe which frames (or approximate time/index) it appears in most clearly. If any object is missing, please state so.
[VLM A | thinking] Based on the provided frames, here is the status of the requested objects:

*   **TV**: Present; most clearly visible in frame 1.
*   **Table (Bedside Table)**: Present; most clearly visible in frames 0, 8, and 10.
*   **Fireplace**: Not present.
*   **Bed**: Present; most clearly visible in frames 0, 8, and 9.
*   **Chair**: Present; most clearly visible in frame 7 (and reflected in the mirror in frame 5).
Based on the provided frames, here is the status of the requested objects:

*   **TV**: Present; most clearly visible in frame 1.
*   **Table (Bedside Table)**: Present; most clearly visible in frames 0, 8, and 10.
*   **Fireplace**: Not present.
*   **Bed**: Present; most clearly visible in frames 0, 8, and 9.
*   **Chair**: Present; most clearly visible in frame 7 (and reflected in the mirror in frame 5).

# Subsample frames for reconstruction (max 64 frames)
stride = len(InputImages) // 63
recon_frames = [InputImages[i] for i in range(0, len(InputImages), stride)]
# Ensure we have exactly or fewer than 64 frames
if len(recon_frames) > 64:
    recon_frames = recon_frames[:64]

# Perform 3D reconstruction
recon = tools.Reconstruct.Reconstruct(recon_frames)
print(f"Reconstruction complete. Number of frames: {recon.num_frames}, Metric scale: {recon.metric_scale}")

Reconstruction complete. Number of frames: 64, Metric scale: 3.796855926513672

# 1. Segment the Chair in the frame identified by VLM (index 7 of the subset)
chair_frame_idx = 3360
chair_img = InputImages[chair_frame_idx]
chair_box_str = vlm.locate(chair_img, "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the armchair. Reply with ONLY the numbers.")
print(f"Chair box string: {chair_box_str}")

seg_chair = None
if "Not visible" not in chair_box_str:
    try:
        coords = [float(x) for x in chair_box_str.replace(',', ' ').split()]
        W, H = chair_img.width, chair_img.height
        chair_box_px = tools.Geometry.normalized_to_pixel(coords, W, H)
        seg_chair = tools.SAM3.segment_image_by_box(chair_img, chair_box_px, label="chair")
        print(f"Successfully segmented chair. Area: {seg_chair.get_mask(seg_chair.frame_indices[0], 0).sum()} pixels")
    except Exception as e:
        print(f"Failed to segment chair: {e}")

# 2. Re-verify Fireplace
# Looking at survey frames, there is no obvious fireplace. 
# I'll try one more text segmentation on a frame that shows the wall.
fp_img = InputImages[2580]
try:
    seg_fp = tools.SAM3.segment_image_by_text(fp_img, "fireplace", label="fireplace")
    print(f"Fireplace segmented. Area: {seg_fp.get_mask(seg_fp.frame_indices[0], 0).sum()} pixels")
except:
    print("Fireplace not found via text segmentation.")
    seg_fp = None

# Store all masks in a dictionary for easier access
# Note: we need to make sure we have the masks from the previous steps
# In Step 3, we had masks for tv, table, bed.
# I will re-run those to be safe and consistent in one object.
all_segs = {}
target_objects = {
    "tv": {"prompt": "television", "frame_idx": 560},
    "table": {"prompt": "bedside table", "frame_idx": 0},
    "bed": {"prompt": "bed", "frame_idx": 0},
}

for label, info in target_objects.items():
    try:
        all_segs[label] = tools.SAM3.segment_image_by_text(InputImages[info["frame_idx"]], info["prompt"], label=label)
    except:
        print(f"Failed to segment {label}")

if seg_chair:
    all_segs["chair"] = seg_chair
if seg_fp:
    all_segs["fireplace"] = seg_fp

print(f"Final segmented objects: {list(all_segs.keys())}")

[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 armchair. Reply with ONLY the numbers.
[VLM A | locate] Not visible
No armchair is present in the image.
Chair box string: Not visible
No armchair is present in the image.
Fireplace not found via text segmentation.
Final segmented objects: ['tv', 'table', 'bed']

# Accurate frame indices from the key frame mapping
# TV: Key frame #4 -> InputImages[560]
# Table: Key frame #1 -> InputImages[0]
# Bed: Key frame #1 -> InputImages[0]
# Fireplace: Key frame #14 -> InputImages[2427]
# Chair: Key frame #20 -> InputImages[3546]

target_objects = {
    "tv": {"prompt": "television", "frame_idx": 560},
    "table": {"prompt": "bedside table", "frame_idx": 0},
    "bed": {"prompt": "bed", "frame_idx": 0},
    "fireplace": {"prompt": "fireplace", "frame_idx": 2427},
    "chair": {"prompt": "armchair", "frame_idx": 3546}
}

all_segs = {}
for label, info in target_objects.items():
    try:
        img = InputImages[info["frame_idx"]]
        seg = tools.SAM3.segment_image_by_text(img, info["prompt"], label=label)
        # Verify mask is not empty
        fi = seg.frame_indices[0]
        area = seg.get_mask(frame=fi, object=0).sum()
        if area > 0:
            all_segs[label] = seg
            print(f"Successfully segmented {label} in frame {info['frame_idx']}. Area: {area} pixels")
        else:
            print(f"Segmentation for {label} in frame {info['frame_idx']} resulted in an empty mask.")
    except Exception as e:
        print(f"Failed to segment {label} in frame {info['frame_idx']}: {e}")

# If chair or fireplace still failed, try VLM grounding as a fallback
if "chair" not in all_segs:
    print("Attempting VLM grounding for chair...")
    chair_img = InputImages[3546]
    chair_box_str = vlm.locate(chair_img, "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the armchair. Reply with ONLY the numbers.")
    if "Not visible" not in chair_box_str:
        try:
            coords = [float(x) for x in chair_box_str.replace(',', ' ').split()]
            W, H = chair_img.width, chair_img.height
            chair_box_px = tools.Geometry.normalized_to_pixel(coords, W, H)
            all_segs["chair"] = tools.SAM3.segment_image_by_box(chair_img, chair_box_px, label="chair")
            print("Successfully segmented chair using VLM grounding.")
        except Exception as e:
            print(f"VLM grounding for chair failed: {e}")

if "fireplace" not in all_segs:
    print("Attempting VLM grounding for fireplace...")
    fp_img = InputImages[2427]
    fp_box_str = vlm.locate(fp_img, "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the fireplace or mantelpiece. Reply with ONLY the numbers.")
    if "Not visible" not in fp_box_str:
        try:
            coords = [float(x) for x in fp_box_str.replace(',', ' ').split()]
            W, H = fp_img.width, fp_img.height
            fp_box_px = tools.Geometry.normalized_to_pixel(coords, W, H)
            all_segs["fireplace"] = tools.SAM3.segment_image_by_box(fp_img, fp_box_px, label="fireplace")
            print("Successfully segmented fireplace using VLM grounding.")
        except Exception as e:
            print(f"VLM grounding for fireplace failed: {e}")

print(f"Final segmented objects: {list(all_segs.keys())}")

Successfully segmented tv in frame 560. Area: 16308 pixels
Successfully segmented table in frame 0. Area: 9709 pixels
Successfully segmented bed in frame 0. Area: 16434 pixels
Failed to segment fireplace in frame 2427: SAM3.segment_image_by_text(prompt='fireplace') produced no usable mask on this image (frame_index=2427, confidence_threshold=0.3). SAM3 either detected nothing matching the prompt, or its mask logits collapsed to all background. Try one of:
  - a different frame from the same video (this frame may be in a transient state),
  - a more specific prompt (e.g. an attribute + noun),
  - lowering confidence_threshold,
  - segment_image_by_box(image, [x1, y1, x2, y2]) with a known box location.
Failed to segment chair in frame 3546: SAM3.segment_image_by_text(prompt='armchair') produced no usable mask on this image (frame_index=3546, confidence_threshold=0.3). SAM3 either detected nothing matching the prompt, or its mask logits collapsed to all background. Try one of:
  - a different frame from the same video (this frame may be in a transient state),
  - a more specific prompt (e.g. an attribute + noun),
  - lowering confidence_threshold,
  - segment_image_by_box(image, [x1, y1, x2, y2]) with a known box location.
Attempting VLM grounding for chair...
[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 armchair. Reply with ONLY the numbers.
[VLM A | locate] 758 756 1000 1000
Successfully segmented chair using VLM grounding.
Attempting VLM grounding for fireplace...
[VLM Q | locate] Before answering, first check whether what th

import numpy as np

# 1. Define reconstruction frames for each object
# recon.frame_indices = [0, 91, 182, 273, 364, 455, 546, 637, 728, 819, 910, 1001, 1092, 1183, 1274, 1365, 1456, 1547, 1638, 1729, 1820, 1911, 2002, 2093, 2184, 2275, 2366, 2457, 2548, 2639, 2730, 2821, 2912, 3003, 3094, 3185, 3276, 3367, 3458, 3549, 3640, 3731, 3822, 3913, 4004, 4095, 4186, 4277, 4368, 4459, 4550, 4641, 4732, 4823, 4914, 5005, 5096, 5187, 5278, 5369, 5460, 5551, 5642, 5733]
target_frames = {
    "tv": 546,
    "table": 0,
    "bed": 0,
    "fireplace": 2457,
    "chair": 3549
}

# 2. Re-segment objects on these specific frames
object_points = {}
for label, fi in target_frames.items():
    try:
        img = InputImages[fi]
        # Use text segmentation first
        prompt = "television" if label == "tv" else ("bedside table" if label == "table" else ("bed" if label == "bed" else ("fireplace" if label == "fireplace" else "armchair")))
        seg = tools.SAM3.segment_image_by_text(img, prompt, label=label)
        
        # Check if mask is valid
        mask = seg.get_mask(frame=fi, object=0)
        if mask.sum() == 0:
            # Fallback to VLM grounding if text segmentation fails
            box_str = vlm.locate(img, f"Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the {prompt}. Reply with ONLY the numbers.")
            if "Not visible" not in box_str:
                coords = [float(x) for x in box_str.replace(',', ' ').split()]
                box_px = tools.Geometry.normalized_to_pixel(coords, img.width, img.height)
                seg = tools.SAM3.segment_image_by_box(img, box_px, label=label)
        
        # Extract 3D points
        pts = seg.get_masked_points(recon, frame=fi)
        if pts is not None and len(pts) > 0:
            # Filter by confidence if available (recon.points.confidence is N, H, W)
            # Since we have the mask, we can index the confidence map
            # But for simplicity, we'll use all points and take the median/mean if needed.
            object_points[label] = pts
            print(f"Extracted {len(pts)} points for {label} at frame {fi}")
        else:
            print(f"No 3D points found for {label} at frame {fi}")
    except Exception as e:
        print(f"Error processing {label} at frame {fi}: {e}")

# 3. Calculate minimum distance from TV to others
if "tv" in object_points:
    tv_pts = object_points["tv"]
    distances = {}
    for label, pts in object_points.items():
        if label == "tv": continue
        
        # Compute minimum pairwise distance
        # For efficiency with large point clouds, we can subsample or use a faster method
        # Here we'll use a sampled approach to avoid memory issues
        sampled_tv = tv_pts[np.random.choice(len(tv_pts), min(1000, len(tv_pts)), replace=False)]
        sampled_obj = pts[np.random.choice(len(pts), min(1000, len(pts)), replace=False)]
        
        # Compute distance matrix
        diff = sampled_tv[:, np.newaxis, :] - sampled_obj[np.newaxis, :, :]
        dist_matrix = np.linalg.norm(diff, axis=2)
        min_dist = np.min(dist_matrix)
        distances[label] = min_dist
        print(f"Min distance TV to {label}: {min_dist:.3f}m")
    
    closest_obj = min(distances, key=distances.get)
    print(f"Closest object to TV: {closest_obj}")
else:
    print("TV points not found. Cannot compute distances.")

Extracted 14613 points for tv at frame 546
Extracted 9709 points for table at frame 0
Extracted 16434 points for bed at frame 0
Extracted 63601 points for fireplace at frame 2457
Extracted 13190 points for chair at frame 3549
Min distance TV to table: 3.054m
Min distance TV to bed: 2.040m
Min distance TV to fireplace: 2.836m
Min distance TV to chair: 5.285m
Closest object to TV: bed

# Create a combined PerFrameMask for BEV rendering
# Since we have masks on different frames, we can't just pass a dict.
# However, we can just render the BEV with the masks we have.
# The render_bev tool can take a PerFrameMask. I will create a simple one.

# To render all objects in one BEV, I'll need to segment them all on the same frame or 
# use the masks from the frames they were found in.
# The render_bev tool typically expects masks for the reconstructed frames.

# Let's just use the masks we already have.
# I'll use the masks from the target_frames.
# Since render_bev expects a PerFrameMask object, I'll just use the one from the TV
# and then add the others if possible, or just rely on the calculated distances
# since they are quite clear. 

# Actually, I can just render the BEV with the TV mask to see the camera path and TV position.
# But to see all objects, I'd need a multi-object PerFrameMask.
# Let's just do a final check of the distances.
# TV to Bed: 2.04m
# TV to Fireplace: 2.84m
# TV to Table: 3.05m
# TV to Chair: 5.29m

# The difference between bed and fireplace is ~0.8m, which is significant.
# I am confident in the result.

print(f"Final Distances:\nBed: {distances['bed']:.3f}m\nFireplace: {distances['fireplace']:.3f}m\nTable: {distances['table']:.3f}m\nChair: {distances['chair']:.3f}m")
ReturnAnswer("C")

Final Distances:
Bed: 2.040m
Fireplace: 2.836m
Table: 3.054m
Chair: 5.285m
[ReturnAnswer] Answer submitted: C