Omni3D — Gemma4-31B — sample 180

# 1. Locate the toilet paper and the toilet
tp_coords = vlm.locate(InputImages[0], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the toilet paper roll. Reply with ONLY the numbers.")
toilet_coords = vlm.locate(InputImages[0], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the toilet. Reply with ONLY the numbers.")

print(f"Toilet paper coords: {tp_coords}")
print(f"Toilet coords: {toilet_coords}")

# Convert normalized coordinates to pixels
W, H = InputImages[0].width, InputImages[0].height

# Parse tp_coords
tp_box_norm = [float(x) for x in tp_coords.replace(',', ' ').split()]
tp_box = tools.Geometry.normalized_to_pixel(tp_box_norm, W, H)

# Parse toilet_coords
toilet_box_norm = [float(x) for x in toilet_coords.replace(',', ' ').split()]
toilet_box = tools.Geometry.normalized_to_pixel(toilet_box_norm, W, H)

# 2. Segment the objects using SAM3
seg_tp = tools.SAM3.segment_image_by_box(InputImages[0], tp_box, label="toilet paper")
seg_toilet = tools.SAM3.segment_image_by_box(InputImages[0], toilet_box, label="toilet")

# 3. Verify masks
fi = InputImages[0].frame_index
vis_tp = seg_tp.visualize(fi)
vis_toilet = seg_toilet.visualize(fi)

show([InputImages[0], vis_tp, vis_toilet])

# Programmatic check for empty masks
for seg, name in [(seg_tp, "toilet paper"), (seg_toilet, "toilet")]:
    mask = seg.get_mask(frame=fi, object=0)
    area = mask.sum()
    print(f"{name} mask area: {area} pixels")

[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 paper roll. Reply with ONLY the numbers.
[VLM A | locate] 717 98 806 245
[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] 153 83 431 751
Toilet paper coords: 717 98 806 245
Toilet coords: 153 83 431 751
 [image: show_0001] 
toilet paper mask area: 3343 pixels
toilet mask area: 50567 pixels

# 1. Reconstruct the 3D scene
recon = tools.Reconstruct.Reconstruct([InputImages[0]])

# 2. Estimate the floor plane
# Use the point cloud and confidence from the first (and only) frame
points = recon.points[0]
confidence = recon.points.confidence[0]

# Fit ground plane using RANSAC
floor_normal, floor_mask = tools.Geometry.fit_ground_plane_ransac(points.reshape(-1, 3), confidence.reshape(-1))

# The floor is approximately a horizontal plane in the world frame (Y = constant)
# We can estimate the floor height by taking the median Y of the points in the floor mask
floor_points = points.reshape(-1, 3)[floor_mask]
y_floor = np.median(floor_points[:, 1])

print(f"Estimated floor height (y_floor): {y_floor}")
print(f"Floor normal: {floor_normal}")

# Sanity check: the normal should be close to [0, 1, 0] or [0, -1, 0]

Estimated floor height (y_floor): 0.7705590724945068
Floor normal: [ 0.8029311  -0.06099614 -0.5929428 ]

# 1. Robust Floor Height Estimation
# Instead of RANSAC (which hit a wall), we take the lower percentile of Y values
all_points = recon.points[0].reshape(-1, 3)
all_conf = recon.points.confidence[0].reshape(-1)
valid_points = all_points[all_conf > 0.5]
y_floor_robust = np.percentile(valid_points[:, 1], 5) # 5th percentile as floor level
print(f"Robust floor height (y_floor): {y_floor_robust}")

# 2. Vertical Intersection Analysis
tp_x, tp_y, tp_z = tp_centroid
epsilon = 0.15 # 15cm tolerance for the width of the roll

# Calculate horizontal distance from each toilet point to the TP center
dist_sq = (toilet_points[:, 0] - tp_x)**2 + (toilet_points[:, 2] - tp_z)**2
mask_below = (toilet_points[:, 1] < tp_y)
mask_aligned = (dist_sq < epsilon**2)

intersecting_points = toilet_points[mask_below & mask_aligned]

if len(intersecting_points) > 0:
    y_impact = np.max(intersecting_points[:, 1])
    print(f"Intersection found! Highest toilet point below TP: {y_impact}")
    
    if y_impact > y_floor_robust:
        result = "toilet"
    else:
        result = "floor"
else:
    print("No intersection with toilet found on the vertical path.")
    result = "floor"

print(f"Final Result: {result}")

# 3. Sanity Check with BEV (using only the toilet mask to avoid the previous error)
bev_toilet = recon.render_bev(masks=seg_toilet, labels=["toilet"])
show(bev_toilet)

Robust floor height (y_floor): 1.1920928959241461e-08
No intersection with toilet found on the vertical path.
Final Result: floor
 [image: show_0002] 

ReturnAnswer("floor")

[ReturnAnswer] Answer submitted: floor