Highlights / MMSI / sample 76

MMSI — Gemma4-31B — sample 76

Keyframes
Question
Assuming that in Figure 4, the wall where the sink is located and the direction pointing outward from the wall is east, then in Figure 1, what is the position of the door relative to the sink in Figure 4? Options: A: Northeast, B: Southwest, C: Southeast, D: Northwest Answer with the option's letter from the given choices directly.
Ground truth A
SpatialClaw A ✓
Agent wins

No-tool Baseline

✗ Incorrect

To determine the position of the door relative to the sink, we need to establish a coordinate system based on the information provided.

  1. Establish Directions in Figure 4:

    • The prompt states that the wall where the sink is located is the reference, and the direction pointing outward from this wall is East.
    • Therefore, the wall with the sink is the West wall.
    • When looking at the West wall (as we are in Figure 4), the direction we are facing is West.
    • In this orientation:
      • North is to the right.
      • South is to the left.
      • East is behind us (the direction pointing away from the wall).

  2. Locate the Door relative to the Sink in Figure 4:

    • In Figure 4, the sink is located on the West wall.
    • The door is located to the left of the sink.
    • Since left is South, the door is south of the sink.
    • Observing the image, the door is on a wall that is perpendicular to the sink's wall. This means the door is on the South wall.
    • Because the door is on the South wall and the sink is on the West wall, the door is located to the East of the plane of the West wall (as it is inside the room).

  3. Combine the Directions:

    • The door is South (to the left) and East (on the adjacent wall, moving away from the West wall) relative to the sink.
    • Therefore, the position of the door relative to the sink is Southeast.

Comparing this to the options: A: Northeast B: Southwest C: Southeast D: Northwest

The correct option is C.

C

Single-pass code

✗ Incorrect
Purpose
Identify the spatial layout of the scene across the four images to determine the position of the door relative to the sink.
Reasoning
1. **Reference Frame (Figure 4)**: The prompt states that the wall with the sink is the West wall (since the direction pointing outward from it is East). - Facing the sink (facing West), the right side is North and the left side is South. - In Figure 4, the ladder is to the right of the sink, meaning the ladder is North of the sink. 2. **Connecting Figure 4 to Figure 2**: Figure 2 also shows a ladder on the right and a wooden door in the background. If the ladder is to the right, the camera in Figure 2 is also facing West. The door is therefore on the West wall. 3. **Connecting Figure 2 to Figure 1**: Figure 1 shows the wooden door on the left and the elevator on the right. If the camera is facing West, the door is to the South and the elevator is to the North. 4. **Synthesizing the West Wall Layout**: On the West wall, from South to North, we have the Door, then likely the Sink (since the ladder is North of it), then the Elevator. 5. **Relative Position**: The door is to the South of the sink. Since the door is on the same wall as the sink (West wall), its position relative to the sink is primarily South. However, looking at Figure 1 and Figure 4, the door is at the end of the corridor/room. 6. Let's use the VLM to confirm if the door is strictly South or if it's shifted (e.g., Southwest). In Figure 1, the door is on the left. If we are facing West, the door is South. If the sink is further North on that same wall, the door is South of the sink. I will use `vlm.ask_with_thinking` to confirm the relative positions of the door, sink, and elevator.
Next Goal
Confirm the relative positions of the door and sink using the VLM.
Code
# Use the VLM to reason about the spatial layout across all frames.
layout_analysis = vlm.ask_with_thinking(
    InputImages, 
    "Analyze the 4 images to determine the spatial layout of the room. "
    "Figure 1 shows a wooden door and an elevator. "
    "Figure 2 shows a ladder and the wooden door. "
    "Figure 4 shows a sink and a ladder. "
    "Assuming the wall with the sink in Figure 4 is the West wall (outward is East), "
    "determine the relative positions (North, South, East, West) of the door, the sink, and the elevator. "
    "Specifically, where is the door located relative to the sink?"
)
print(layout_analysis)

# Based on the reasoning:
# Fig 4: Facing West. Sink is West. Ladder is North.
# Fig 2: Facing West. Ladder is North. Door is West.
# Fig 1: Facing West. Door is South. Elevator is North.
# So on the West wall, from South to North: Door -> Sink -> Elevator.
# Therefore, the door is South of the sink. 
# Since they are on the same wall, it's primarily South. 
# Let's check if the door is further 'back' or 'forward' (East/West).
# They are both on the West wall, so they have similar Easting.
# Looking at the options: Northeast, Southwest, Southeast, Northwest.
# If the door is South and slightly 'further' in some direction, we need to be precise.
# In Figure 1, the door is to the left of the elevator. 
# In Figure 4, the sink is to the left of the ladder.
# If the door is at the southern end of the west wall and the sink is further north,
# then the door is South of the sink.
# Is it Southwest or Southeast? 
# The door is on the wall. The sink is on the wall. 
# If the door is 'before' the sink when moving North, it's South.
# Let's re-read the options. They are all diagonal.
# Maybe the door is not on the same wall?
# In Fig 1, the door and elevator look like they are on the same wall.
# In Fig 4, the sink is on a wall.
# Let's see if the door is on a different wall.
# In Fig 2, the door is straight ahead, and the ladder is to the right.
# In Fig 4, the sink is straight ahead, and the ladder is to the right.
# This confirms both are on the West wall.
# If the door is to the South of the sink, and we must choose a diagonal...
# Let's look at the depth.
# In Fig 2, the door is further away than the ladder.
# In Fig 4, the sink is closer than the ladder? No, they are similar.
# Actually, if the door is at the far end of the room (West) and the sink is also on the West wall,
# the door's position relative to the sink would be South.
# Let's check the VLM output.
Error
Cell execution timed out after 600.5s (limit 600s).
Final answer
B

Structured tool-call

✗ Incorrect
Plan

Task Analysis The objective is to determine the cardinal direction (Northeast, Southwest, Southeast, or Northwest) of a door (visible in Figure 1) relative to a sink (visible in Figure 4). A custom coordinate system is provided:

  • East: The direction pointing outward (normal) from the wall where the sink is located in Figure 4.
  • West: The direction pointing toward the sink wall.
  • North/South: These axes are perpendicular to the East-West axis on the ground plane. Following standard map conventions, North is $90^\circ$ counter-clockwise from East when viewed from above.
  • Target Variable: The quadrant of the vector $\vec{v}_{sink \to door}$ in this East-North coordinate system.

Information Needs

  1. 3D Positions: The world-space 3D coordinates of the sink (from Figure 4) and the door (from Figure 1).
  2. Wall Orientation: The 3D normal vector of the wall containing the sink in Figure 4 to define the "East" vector.
  3. Scene Geometry: A 3D reconstruction to relate the different viewpoints of Figure 1 and Figure 4.
  4. Ground Plane: The world "up" vector (gravity) to ensure the East and North vectors are constrained to the horizontal plane.

Computation Plan

  1. Object Identification and Segmentation:

    • Use vlm.locate to find the bounding boxes of the sink in InputImages[3] (Figure 4) and the door in InputImages[0] (Figure 1).
    • Use tools.SAM3.segment_image_by_box to generate precise masks for the sink and the door.
    • Visually verify the masks using show() and seg.visualize().

  2. 3D Reconstruction:

    • Perform a full reconstruction of the scene using all available frames: recon = tools.Reconstruct.Reconstruct(InputImages).

  3. Extracting 3D Centroids:

    • Calculate the 3D world position of the sink: pos_sink = seg_sink.get_centroid_3d(recon, frame=InputImages[3].frame_index).
    • Calculate the 3D world position of the door: pos_door = seg_door.get_centroid_3d(recon, frame=InputImages[0].frame_index).

  4. Defining the Cardinal Axes:

    • East Vector ($\vec{v}_{East}$):
      • Segment the wall area surrounding the sink in Figure 4.
      • Extract the 3D points of the wall using seg_wall.get_masked_points(recon, ...).
      • Use tools.Geometry.fit_ground_plane_ransac or a similar plane-fitting approach on the wall points to find the surface normal.
      • Ensure the normal points outward from the wall (away from the sink's back). This is $\vec{v}_{East}$.
    • Up Vector ($\vec{v}_{Up}$): Use the reconstruction's gravity alignment ($+Y$ axis: [0, 1, 0]).
    • North Vector ($\vec{v}_{North}$): Compute the cross product $\vec{v}{North} = \vec{v}{Up} \times \vec{v}_{East}$ to establish a right-handed coordinate system.

  5. Relative Position Calculation:

    • Compute the relative vector: $\vec{v}{rel} = pos{door} - pos_{sink}$.
    • Project $\vec{v}_{rel}$ onto the cardinal axes:
      • $East_component = \vec{v}{rel} \cdot \vec{v}{East}$
      • $North_component = \vec{v}{rel} \cdot \vec{v}{North}$

  6. Direction Determination:

    • If $East_component > 0$ and $North_component > 0 \rightarrow$ Northeast.
    • If $East_component < 0$ and $North_component > 0 \rightarrow$ Northwest.
    • If $East_component < 0$ and $North_component < 0 \rightarrow$ Southwest.
    • If $East_component > 0$ and $North_component < 0 \rightarrow$ Southeast.

Verification

  • BEV Inspection: Render a BEV plot using recon.render_bev(masks=seg_all). Manually verify if the door is "in front of" (East) or "behind" (West) the sink wall, and "left" or "right" (North/South) relative to the sink.
  • Mask Validation: Ensure the sink and door masks are not empty and accurately cover the objects.
  • Normal Check: Verify the $\vec{v}_{East}$ vector points away from the wall in the BEV.

Fallbacks

  • If the wall cannot be segmented cleanly, use the sink's orientation (the direction the basin faces) as a proxy for the East vector.
  • If reconstruction is noisy, use vlm.ask_with_thinking on a sequence of frames to qualitatively describe the relative movement/position of the camera between the door and the sink.

CHECKLIST

[
  {"priority": "HIGH", "description": "Verify that the sink and door are correctly segmented in their respective frames"},
  {"priority": "HIGH", "description": "Confirm the 'East' vector is correctly identified as the outward normal of the sink wall"},
  {"priority": "MEDIUM", "description": "Cross-validate the computed quadrant (NE/NW/SE/SW) using the BEV rendering"},
  {"priority": "LOW", "description": "Check that the 3D centroids are physically plausible and not outliers"}
]
Step 0
LLM · Tool Call
Tool Call
{
  "tool": "feedback.ask_vlm",
  "args": {
    "visual_input": "InputImages[3]",
    "question": "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sink. Reply with ONLY the numbers."
  }
}
Step 2
LLM · Tool Call
Purpose
Locate the sink in Figure 4 (InputImages[3]) to get coordinates for segmentation.
Reasoning
I need to find the sink in the image to create a segmentation mask. I'll start by getting its bounding box using the VLM.
Next Goal
Use the bounding box to segment the sink.
Tool Call
{
  "tool": "vlm.locate",
  "args": {
    "visual_input": "InputImages[3]",
    "question": "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sink. 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 sink. Reply with ONLY the numbers.
[VLM A | locate] 381 244 665 582
Error
[LLMClient] VLM attempt 1/5 failed (APIConnectionError: Connection error.), retrying...
Step 3
LLM · Tool Call
Purpose
Convert the normalized bounding box coordinates of the sink to pixel coordinates.
Reasoning
The VLM provided the bounding box in 0-1000 normalized scale. To use SAM3's `segment_image_by_box` method, I need the coordinates in pixels.
Next Goal
Segment the sink using the pixel coordinates.
Tool Call
{
  "tool": "tools.Geometry.normalized_to_pixel",
  "args": {
    "coords": [
      381,
      244,
      665,
      582
    ],
    "width": "InputImages[3].width",
    "height": "InputImages[3].height"
  }
}
Step 4
LLM · Tool Call
Purpose
Segment the sink in Figure 4 (InputImages[3]) using the pixel bounding box.
Reasoning
I have the pixel coordinates for the sink's bounding box. Now I will use SAM3 to create a precise mask for the sink.
Next Goal
Locate the door in Figure 1 (InputImages[0]).
Tool Call
{
  "tool": "tools.SAM3.segment_image_by_box",
  "args": {
    "image": "InputImages[3]",
    "box": "result_3",
    "label": "sink"
  }
}
Step 5
LLM · Tool Call
Purpose
Locate the door in Figure 1 (InputImages[0]) to obtain coordinates for segmentation.
Reasoning
To find the relative position of the door to the sink, I first need to identify the door's location in the first image.
Next Goal
Convert the door's normalized coordinates to pixels.
Tool Call
{
  "tool": "vlm.locate",
  "args": {
    "visual_input": "InputImages[0]",
    "question": "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the door. 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 door. Reply with ONLY the numbers.
[VLM A | locate] 0 0 336 641
Step 6
LLM · Tool Call
Purpose
Convert the normalized bounding box coordinates of the door to pixel coordinates.
Reasoning
The VLM provided the bounding box for the door in 0-1000 normalized scale. I need these in pixels to perform segmentation with SAM3.
Next Goal
Segment the door using the pixel coordinates.
Tool Call
{
  "tool": "tools.Geometry.normalized_to_pixel",
  "args": {
    "coords": [
      0,
      0,
      336,
      641
    ],
    "width": "InputImages[0].width",
    "height": "InputImages[0].height"
  }
}
Step 7
LLM · Tool Call
**Purpose**: Segment the door in Figure 1 (InputImages[0]) using the pixel bounding box. **Reasoning**: I have the pixel coordinates for the door's bounding box. Now I will use SAM3 to create a precise mask for the door. **Next Goal**: Perform a 3D reconstruction of the scene using all available frames. **Tool Call (translated)**: ```python result_7 = tools.SAM3.segment_image_by_box(image=InputImages[0], box=result_6, label='door') ```
Step 8
LLM · Tool Call
Purpose
Segment the door in Figure 1 (InputImages[0]) using the pixel bounding box.
Reasoning
I have the pixel coordinates for the door's bounding box. Now I will use SAM3 to create a precise mask for the door.
Next Goal
Perform 3D reconstruction of the scene to relate the sink and the door.
Tool Call
{
  "tool": "tools.SAM3.segment_image_by_box",
  "args": {
    "image": "InputImages[0]",
    "box": "result_6",
    "label": "door"
  }
}
Step 9
LLM · Tool Call
Purpose
Perform 3D reconstruction of the scene to relate the sink and the door in world space.
Reasoning
To determine the relative position and the cardinal directions, I need a consistent 3D world coordinate system. Reconstructing the scene from the provided images will allow me to extract 3D centroids and the orientation of the sink wall.
Next Goal
Extract the 3D centroids of the sink and the door.
Tool Call
{
  "tool": "tools.Reconstruct.Reconstruct",
  "args": {
    "frames": "InputImages"
  }
}
Step 10
LLM · Tool Call
**Purpose**: Extract the 3D world positions of the sink and the door. **Reasoning**: To calculate the relative vector from the sink to the door, I need their 3D centroids in the reconstructed world space. **Next Goal**: Determine the orientation of the sink wall to define the "East" vector. **Tool Call (translated)**: ```python result_10 = [result_4.get_centroid_3d(result_9, frame=3, object='sink'), result_8.get_centroid_3d(result_9, frame=0, object='door')] ```
Step 11
LLM · Tool Call
Purpose
Extract the 3D world positions of the sink and the door.
Reasoning
I need the 3D centroids of the sink (from frame 3) and the door (from frame 0) to calculate the relative displacement vector between them in world space.
Next Goal
Define the "East" vector by finding the normal of the wall where the sink is located.
Tool Call
{
  "tool": "vlm.ask_with_thinking",
  "args": {
    "visual_input": [
      "InputImages[0]",
      "InputImages[3]"
    ],
    "question": "I have a 3D reconstruction of this scene. I need to find the 3D centroids of the sink (in frame 3) and the door (in frame 0). I will use the tool result_4.get_centroid_3d(result_9, frame=3, object=0) and result_8.get_centroid_3d(result_9, frame=0, object=0). Please confirm these are the correct objects."
  }
}
Execute
Output
[VLM Q | thinking] I have a 3D reconstruction of this scene. I need to find the 3D centroids of the sink (in frame 3) and the door (in frame 0). I will use the tool result_4.get_centroid_3d(result_9, frame=3, object=0) and result_8.get_centroid_3d(result_9, frame=0, object=0). Please confirm these are the correct objects.
[VLM A | thinking] Yes, those are the correct objects. Frame 0 shows a door and frame 3 shows a sink.
Step 15
LLM · Tool Call
Purpose
Visualize the sink and the camera trajectory in a Bird's Eye View (BEV) to understand the spatial layout.
Reasoning
I want to see where the sink is relative to the camera's path. Since I couldn't pass multiple `PerFrameMask` objects, I'll start with the sink and the ego trajectory. The camera at frame 0 was near the door, so the trajectory will help me locate the door's general area.
Next Goal
Use the BEV to determine the relative direction of the door.
Tool Call
{
  "tool": "show",
  "args": {
    "image": "result_9.render_bev(masks=result_4, labels=['sink'], ego_trajectory=True)"
  }
}
Execute
Output
 [image: show_0001]
show_0001
Step 16
LLM · Tool Call
Purpose
Determine the relative position of the door to the sink based on the provided cardinal directions.
Reasoning
1. **Define the Coordinate System**: - **East**: The direction pointing outward from the wall where the sink is located. In Figure 4, the camera is facing the sink, and the sink faces the camera. Therefore, the East vector is the direction from the sink to the camera in Figure 4. - From the BEV (result_15), the sink is at approximately $(0, 2.2)$ and Camera 3 (the one in Figure 4) is at approximately $(4.5, 0)$. - $\vec{v}_{East} \approx \text{pos}(\text{Camera 3}) - \text{pos}(\text{Sink}) = (4.5, 0) - (0, 2.2) = (4.5, -2.2)$. - **North**: $90^\circ$ counter-clockwise from East. For a 2D vector $(x, y)$, the $90^\circ$ CCW rotation is $(-y, x)$. - $\vec{v}_{North} \approx (2.2, 4.5)$. 2. **Locate the Door**: - The door is visible in Figure 1. Camera 0 is at $(0, 0)$ and is looking forward (along the +Y axis of the BEV). - In Figure 1, the door is to the left of the camera's view. - Therefore, the door's position is approximately $(-1, 0)$. 3. **Compute the Relative Vector**: - $\vec{v}_{rel} = \text{pos}(\text{Door}) - \text{pos}(\text{Sink}) \approx (-1, 0) - (0, 2.2) = (-1, -2.2)$. 4. **Project onto Cardinal Axes**: - **East Component**: $\vec{v}_{rel} \cdot \vec{v}_{East} \approx (-1)(4.5) + (-2.2)(-2.2) = -4.5 + 4.84 = 0.34$. Since this is positive, the door is **East** of the sink. - **North Component**: $\vec{v}_{rel} \cdot \vec{v}_{North} \approx (-1)(2.2) + (-2.2)(4.5) = -2.2 - 9.9 = -12.1$. Since this is negative, the door is **South** of the sink. 5. **Conclusion**: The relative position is Southeast.
Next Goal
Return the final answer.
Tool Call
{
  "tool": "ReturnAnswer",
  "args": {
    "answer": "C"
  }
}
Execute
Output
[ReturnAnswer] Answer submitted: C
Answer
C

SpaceTools

✗ Incorrect
Tool decision

🛠️ Decided to use 2 tool(s)

tool_calls
[
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 0,
      "obj_name": "door"
    }
  },
  {
    "name": "vlm__detect_one",
    "arguments": {
      "image_index": 3,
      "obj_name": "sink"
    }
  }
]
Tool call

⚡ Executing vlm__detect_one...

tool_name
vlm__detect_one
arguments
{
  "image_index": 0,
  "obj_name": "door"
}
Tool result

✅ vlm__detect_one completed (with generated image)

tool_name
vlm__detect_one
result
Detected 1 instance(s) of 'door': [(0.149, 0.355)] Use $door_detections to reference them.
Tool call

⚡ Executing vlm__detect_one...

tool_name
vlm__detect_one
arguments
{
  "image_index": 3,
  "obj_name": "sink"
}
Tool result

✅ vlm__detect_one completed (with generated image)

tool_name
vlm__detect_one
result
Detected 1 instance(s) of 'sink': [(0.5429999999999999, 0.478)] Use $sink_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": 3,
      "obj_name": "door"
    }
  }
]
Tool call

⚡ Executing vlm__detect_one...

tool_name
vlm__detect_one
arguments
{
  "image_index": 3,
  "obj_name": "door"
}
Tool result

✅ vlm__detect_one completed (with generated image)

tool_name
vlm__detect_one
result
Detected 1 instance(s) of 'door': [(0.106, 0.7809999999999999)] Use $door_detections to reference them.
Synthesizing

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

Reasoning

Based on Figure 4, the wall with the sink is the "east" wall, meaning the direction facing away from it is east. In this frame of reference, the door (located to the left of the sink in Figure 4) is to the north of the sink.

In Figure 1, the door is visible on the left side of the image, while the elevator (which is to the right of the sink area as seen in Figure 4) is on the right. This confirms the door remains to the north of the sink's location. Since the door is further "back" or slightly displaced from the sink's position along the north-south axis, the position of the door relative to the sink is Northwest.

D

Final answer

Based on Figure 4, the wall with the sink is the "east" wall, meaning the direction facing away from it is east. In this frame of reference, the door (located to the left of the sink in Figure 4) is to the north of the sink.

In Figure 1, the door is visible on the left side of the image, while the elevator (which is to the right of the sink area as seen in Figure 4) is on the right. This confirms the door remains to the north of the sink's location. Since the door is further "back" or slightly displaced from the sink's position along the north-south axis, the position of the door relative to the sink is Northwest.

D

Final response

Based on Figure 4, the wall with the sink is the "east" wall, meaning the direction facing away from it is east. In this frame of reference, the door (located to the left of the sink in Figure 4) is to the north of the sink.

In Figure 1, the door is visible on the left side of the image, while the elevator (which is to the right of the sink area as seen in Figure 4) is on the right. This confirms the door remains to the north of the sink's location. Since the door is further "back" or slightly displaced from the sink's position along the north-south axis, the position of the door relative to the sink is Northwest.

D

SpatialClaw

✓ Correct
Plan

Task Analysis

The goal is to determine the relative position (cardinal direction) of a door (seen in Figure 1) with respect to a sink (seen in Figure 4), based on a custom coordinate system defined in Figure 4.

Coordinate System Resolution:

  • Reference Point: The sink in Figure 4.
  • Custom East ($\vec{v}_{East}$): The vector pointing outward (perpendicularly) from the wall where the sink is located in Figure 4.
  • Custom North ($\vec{v}_{North}$): By standard convention, North is $90^\circ$ counter-clockwise from East in the horizontal plane. Given the world gravity-aligned Y-axis as "Up" ($\vec{v}{Up} = [0, 1, 0]$), $\vec{v}{North} = \vec{v}{Up} \times \vec{v}{East}$.
  • Target: The position of the door in Figure 1 relative to the sink in Figure 4.
  • Answer Format: One of the four quadrants: Northeast, Northwest, Southeast, or Southwest.

Information Needs

  1. 3D Centroids: The world-space 3D positions of the sink (from Figure 4) and the door (from Figure 1).
  2. Wall Normal: The 3D normal vector of the wall containing the sink in Figure 4 to define the "East" axis.
  3. 3D Reconstruction: A consistent 3D map of the scene to relate objects across different frames.

Computation Plan

  1. Object Identification and Segmentation:

    • Use vlm.locate on InputImages[3] (Figure 4) to find the bounding box of the sink.
    • Use vlm.locate on InputImages[0] (Figure 1) to find the bounding box of the door.
    • Use tools.SAM3.segment_image_by_box to create masks for the sink in InputImages[3] and the door in InputImages[0].
    • Use show() to visually verify that the masks accurately cover the sink and the door.

  2. 3D Reconstruction:

    • Call recon = tools.Reconstruct.Reconstruct(InputImages) using all 4 frames to establish a global world coordinate system.

  3. Extract 3D Positions:

    • Calculate the 3D centroid of the sink: sink_pos = seg_sink.get_centroid_3d(recon, frame=InputImages.frame_indices[3]).
    • Calculate the 3D centroid of the door: door_pos = seg_door.get_centroid_3d(recon, frame=InputImages.frame_indices[0]).

  4. Define the Custom Coordinate Axes:

    • Segment the wall where the sink is located in InputImages[3] (using vlm.locate and SAM3).
    • Extract the 3D points of the wall: wall_points = seg_wall.get_masked_points(recon, frame=InputImages.frame_indices[3]).
    • Use tools.Geometry.fit_ground_plane_ransac(wall_points, confidence) (or a similar plane-fitting method) to find the wall's normal vector.
    • Ensure the normal $\vec{n}{wall}$ points outward from the wall into the room. This is $\vec{v}{East}$.
    • Define $\vec{v}{North} = \text{np.cross}([0, 1, 0], \vec{v}{East})$.

  5. Determine Relative Direction:

    • Compute the relative vector: $\vec{v}_{rel} = \text{door_pos} - \text{sink_pos}$.
    • Project $\vec{v}_{rel}$ onto the custom axes:
      • $d_{East} = \vec{v}{rel} \cdot \vec{v}{East}$
      • $d_{North} = \vec{v}{rel} \cdot \vec{v}{North}$
    • Determine the quadrant:
      • $d_{East} > 0, d_{North} > 0 \rightarrow$ Northeast (A)
      • $d_{East} < 0, d_{North} > 0 \rightarrow$ Northwest (D)
      • $d_{East} > 0, d_{North} < 0 \rightarrow$ Southeast (C)
      • $d_{East} < 0, d_{North} < 0 \rightarrow$ Southwest (B)

Verification

  • Visual Cross-Check: Render a BEV map using recon.render_bev(masks=all_segs) to visually confirm the relative positions of the sink and the door.
  • Axis Verification: In the BEV, verify that the computed $\vec{v}_{East}$ vector indeed points away from the sink's wall.
  • Consistency Check: Ensure the 3D centroids are not NaN and are physically plausible (e.g., the sink is on the floor/wall, the door is vertical).
  • Mask Audit: Use seg.visualize() to ensure the segmentation of the wall is clean and not including the floor, which would skew the normal vector.

Fallbacks

  • If fit_ground_plane_ransac fails or the wall mask is noisy, I will use the recon.points at the sink's location and the camera's orientation in Figure 4 to estimate the wall's plane.
  • If SAM3 text prompts fail, I will rely on the vlm.locate $\rightarrow$ segment_image_by_box pipeline.

CHECKLIST

[
  {"priority": "HIGH", "description": "Verify that the sink and door masks are correct using show()"},
  {"priority": "HIGH", "description": "Confirm the 'East' vector is correctly calculated as the outward normal of the sink's wall"},
  {"priority": "HIGH", "description": "Ensure the relative vector is calculated as (Door - Sink) and not vice versa"},
  {"priority": "MEDIUM", "description": "Cross-verify the final quadrant using the rendered BEV map"},
  {"priority": "LOW", "description": "Check that 3D centroids are calculated using the median of masked points to avoid outliers"}
]
Condense · step 0
**Purpose**: Locate and segment the sink (Figure 4) and the door (Figure 1) to obtain their 2D masks. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # Locate the sink in Figure 4 (InputImages[3]) sink_bbox_norm = vlm.locate(InputImages[3], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sink. Reply with ONLY the numbers.") print(f"Sink bbox (norm): {sink_bbox_norm}") # Locate the door in Figure 1 (InputImages[0]) door_bbox_norm = vlm.locate(InputImages[0], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the wooden door. Reply with ONLY the numbers.") print(f"Door bbox (norm): {door_bbox_norm}") # Convert normalized coordinates to pixels W3, H3 = InputImages[3].width, InputImages[3].height W0, H0 = InputImages[0].width, InputImages[0].height # Parse sink bbox sink_bbox = tools.Geometry.normalized_to_pixel(eval(sink_bbox_norm), W3, H3) # <-- ERROR # Security violation: Forbidden builtin call: 'eval()'. This operation is not allowed. ```
Condense · step 1
**Purpose**: Locate and segment the sink (Figure 4) and the door (Figure 1) to obtain their 2D masks. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python def parse_coords(coord_str): """Parses a comma-separated string of numbers into a list of floats.""" return [float(x.strip()) for x in coord_str.replace('[', '').replace(']', '').split(',')] # 1. Locate the sink in Figure 4 (InputImages[3]) sink_bbox_norm_str = vlm.locate(InputImages[3], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the sink. Reply with ONLY the numbers.") print(f"Sink bbox string: {sink_bbox_norm_str}") sink_bbox_norm = parse_coords(sink_bbox_norm_str) # <-- ERROR # ValueError: could not convert string to float: '381 248 668 582' ```
Step 2
LLM
Purpose
Locate and segment the sink (Figure 4) and the door (Figure 1) to obtain their 2D masks.
Reasoning
The previous attempt failed because the VLM returned space-separated coordinates instead of comma-separated ones. I will use a more robust parsing method that splits by any whitespace. I will then locate the door, convert both bounding boxes to pixel coordinates, and generate segmentation masks using SAM3.
Next Goal
Reconstruct the scene in 3D and calculate the centroids of the sink and the door.
Execute
Code
def parse_coords(coord_str):
    """Parses a string of numbers (space or comma separated) into a list of floats."""
    # Remove brackets and split by whitespace or commas
    cleaned = coord_str.replace('[', '').replace(']', '').replace(',', ' ')
    return [float(x) for x in cleaned.split()]

# 1. Process the sink (InputImages[3])
# sink_bbox_norm_str was already obtained: "381 248 668 582"
sink_bbox_norm = parse_coords(sink_bbox_norm_str)
W3, H3 = InputImages[3].width, InputImages[3].height
sink_bbox_px = tools.Geometry.normalized_to_pixel(sink_bbox_norm, W3, H3)
seg_sink = tools.SAM3.segment_image_by_box(InputImages[3], sink_bbox_px, "sink")

# 2. Locate and process the door (InputImages[0])
door_bbox_norm_str = vlm.locate(InputImages[0], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the wooden door. Reply with ONLY the numbers.")
print(f"Door bbox string: {door_bbox_norm_str}")
door_bbox_norm = parse_coords(door_bbox_norm_str)
W0, H0 = InputImages[0].width, InputImages[0].height
door_bbox_px = tools.Geometry.normalized_to_pixel(door_bbox_norm, W0, H0)
seg_door = tools.SAM3.segment_image_by_box(InputImages[0], door_bbox_px, "door")

# 3. Visual verification
fi_sink = seg_sink.frame_indices[0]
fi_door = seg_door.frame_indices[0]
vis_sink = seg_sink.visualize(fi_sink)
vis_door = seg_door.visualize(fi_door)

show([InputImages[3], vis_sink, InputImages[0], vis_door])

# Programmatic check for empty masks
sink_area = seg_sink.get_mask(frame=fi_sink, object=0).sum()
door_area = seg_door.get_mask(frame=fi_door, object=0).sum()
print(f"Sink mask area: {sink_area} pixels")
print(f"Door mask area: {door_area} pixels")
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 wooden door. Reply with ONLY the numbers.
[VLM A | locate] 0 0 335 640
Door bbox string: 0 0 335 640
 [image: show_0001] 
Sink mask area: 32531 pixels
Door mask area: 71950 pixels
show_0001
Condense · step 3
**Purpose**: Reconstruct the scene in 3D, calculate centroids for the sink and door, and segment the wall containing the sink to determine the "East" axis. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # [ERROR] Cell execution timed out after 600.5s (limit 600s). 1: # 1. 3D Reconstruction 2: recon = tools.Reconstruct.Reconstruct(InputImages) 3: ... 57: v_north = np.cross(v_up, v_east) 58: v_north = v_north / np.linalg.norm(v_north) 59: print(f"Custom North vector (v_north): {v_north}") ```
Condense · step 4
**Purpose**: Reconstruct the scene and determine the 3D positions of the sink and door, and the normal of the wall containing the sink. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # 1. 3D Reconstruction recon = tools.Reconstruct.Reconstruct(InputImages) # 2. Extract 3D positions fi_sink = seg_sink.frame_indices[0] # <-- ERROR # NameError: name 'seg_sink' is not defined ```
Step 5
LLM
Purpose
Re-segment the sink and door, extract their 3D positions, and determine the "East" axis from the sink's wall normal.
Reasoning
I need to recover the segmentation masks since they were lost in previous errors. I will then use the 3D reconstruction to find the centroids of the sink and door. To define "East", I'll segment the wall behind the sink, fit a plane to its 3D points, and ensure the normal points outward (towards the camera).
Next Goal
Calculate the relative position of the door with respect to the sink in the custom coordinate system and determine the final quadrant.
Execute
Code
# 1. Re-segment the sink and door
def parse_coords(coord_str):
    cleaned = coord_str.replace('[', '').replace(']', '').replace(',', ' ')
    return [float(x) for x in cleaned.split()]

# Sink in Figure 4 (InputImages[3])
sink_bbox_norm = [381, 248, 668, 582]
W3, H3 = InputImages[3].width, InputImages[3].height
sink_bbox_px = tools.Geometry.normalized_to_pixel(sink_bbox_norm, W3, H3)
seg_sink = tools.SAM3.segment_image_by_box(InputImages[3], sink_bbox_px, "sink")

# Door in Figure 1 (InputImages[0])
door_bbox_norm = [0, 0, 335, 640]
W0, H0 = InputImages[0].width, InputImages[0].height
door_bbox_px = tools.Geometry.normalized_to_pixel(door_bbox_norm, W0, H0)
seg_door = tools.SAM3.segment_image_by_box(InputImages[0], door_bbox_px, "door")

# 2. Get 3D centroids
fi_sink = seg_sink.frame_indices[0]
fi_door = seg_door.frame_indices[0]
sink_pos = seg_sink.get_centroid_3d(recon, frame=fi_sink)
door_pos = seg_door.get_centroid_3d(recon, frame=fi_door)
print(f"Sink 3D position: {sink_pos}")
print(f"Door 3D position: {door_pos}")

# 3. Define East axis (outward normal of the sink wall)
# Segment the wall behind the sink in Figure 4
wall_bbox_norm_str = vlm.locate(InputImages[3], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the wall that the sink is mounted on. Reply with ONLY the numbers.")
wall_bbox_norm = parse_coords(wall_bbox_norm_str)
wall_bbox_px = tools.Geometry.normalized_to_pixel(wall_bbox_norm, W3, H3)
seg_wall = tools.SAM3.segment_image_by_box(InputImages[3], wall_bbox_px, "wall")

# Extract wall points and fit plane
wall_points = seg_wall.get_masked_points(recon, frame=fi_sink)
# Use only high-confidence points
conf_mask = recon.points[fi_sink][seg_wall.get_mask(frame=fi_sink, object=0)] # This is wrong, recon.points is (H,W,3)
# Correct way to get points with confidence:
mask = seg_wall.get_mask(frame=fi_sink, object=0)
points_3d = recon.points[fi_sink][mask]
confidence = recon.points.confidence[fi_sink][mask]
valid_points = points_3d[confidence > 0.5]

normal, _ = tools.Geometry.fit_ground_plane_ransac(valid_points, confidence[confidence > 0.5])

# Ensure normal points OUTWARD from the wall (towards the camera)
# Camera position at fi_sink
cam_pos = recon.extrinsics[fi_sink][:3, 3]
vec_wall_to_cam = cam_pos - sink_pos
if np.dot(normal, vec_wall_to_cam) < 0:
    normal = -normal

v_east = normal / np.linalg.norm(normal)
print(f"Custom East vector (v_east): {v_east}")

# 4. Define North axis
v_up = np.array([0, 1, 0])
v_north = np.cross(v_up, v_east)
v_north = v_north / np.linalg.norm(v_north)
print(f"Custom North vector (v_north): {v_north}")
Output
Sink 3D position: [5.162794  0.5542624 0.8041106]
Door 3D position: [-1.0741154  1.4865434 -2.558208 ]
[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 wall that the sink is mounted on. Reply with ONLY the numbers.
[VLM A | locate] 100 0 760 950
Custom East vector (v_east): [-0.369247    0.42055562 -0.8287278 ]
Custom North vector (v_north): [-0.91343337  0.          0.40698831]
Condense · step 6
**Purpose**: Calculate the relative position of the door with respect to the sink in the custom coordinate system and determine the final quadrant. **Reasoning**: [errored — condensed] **Next Goal**: [errored — condensed] **Code**: ```python # 1. Refine East and North vectors to be purely horizontal v_east_raw = v_east v_east_horiz = np.array([v_east_raw[0], 0, v_east_raw[2]]) v_east_horiz = v_east_horiz / np.linalg.norm(v_east_horiz) v_up = np.array([0, 1, 0]) v_north_horiz = np.cross(v_up, v_east_horiz) v_north_horiz = v_north_horiz / np.linalg.norm(v_north_horiz) print(f"Refined East vector: {v_east_horiz}") print(f"Refined North vector: {v_north_horiz}") # 2. Compute relative vector v_rel = door_pos - sink_pos print(f"Relative vector (Sink -> Door): {v_rel}") # 3. Project relative vector onto custom axes d_east = np.dot(v_rel, v_east_horiz) d_north = np.dot(v_rel, v_north_horiz) print(f"Projection East: {d_east:.3f}") print(f"Projection North: {d_north:.3f}") # 4. Determine quadrant if d_east > 0 and d_north > 0: quadrant = "Northeast" option = "A" elif d_east < 0 and d_north > 0: quadrant = "Northwest" option = "D" elif d_east > 0 and d_north < 0: quadrant = "Southeast" option = "C" else: quadrant = "Southwest" option = "B" print(f"Result: {quadrant} ({option})") # 5. Visual Verification with BEV # Combine masks for BEV all_masks = [] all_labels = [] # We need masks for all reconstructed frames for BEV to work well, # but we can just pass the specific ones we have. # The render_bev expects a PerFrameMask or (N, N_obj, H, W) # Let's create a simple combined PerFrameMask if possible, or just use raw arrays. # Actually, we can just pass the individual ones in a list if we wrap them. # To make it easy, let's just use the labels we have. # Since we only have masks for specific frames, we'll just render a BEV of the reconstruction. # We can use the masks we already have. bev_vis = recon.render_bev(masks=[seg_sink, seg_door], labels=["sink", "door"]) # <-- ERROR # TypeError: Could not convert object to sequence ```
Step 7
LLM
Purpose
Calculate the relative position of the door with respect to the sink in the custom coordinate system and determine the final quadrant.
Reasoning
1. **Coordinate System**: - $\vec{v}_{East}$ was defined as the outward normal of the sink's wall (pointing into the room/towards the camera in Figure 4). - $\vec{v}_{North}$ was defined as $\vec{v}_{Up} \times \vec{v}_{East}$, creating a standard right-handed horizontal coordinate system. 2. **Quantitative Results**: - Relative vector $\vec{v}_{rel} = \text{door\_pos} - \text{sink\_pos} = [-6.237, 0.932, -3.362]$. - Projection onto $\vec{v}_{East}$ ($d_{East}$) = $5.610$ (Positive). - Projection onto $\vec{v}_{North}$ ($d_{North}$) = $4.329$ (Positive). 3. **Conclusion**: Since both the East and North components of the relative vector are positive, the door is located to the Northeast of the sink.
Next Goal
Return the final answer.
Execute
Code
ReturnAnswer("A")
Output
[ReturnAnswer] Answer submitted: A
Answer
A
Evaluation
Ground truth: A · Agent: A