3D Object Detection for Physical AI Applications

A robot needs more than image classification.

It needs to know:

• What object is present?
• Where is it?
• How large is it?
• How is it oriented?

3D object detection answers these questions in physical space.

3D Detection Pipeline

Camera / LiDAR
      |
      v
Preprocessing
      |
      v
Feature Extraction
      |
      v
3D Detection Model
      |
      v
3D Bounding Boxes
      |
      v
Tracking / Planning

A 3D bounding box can contain:

(x, y, z)
(width, height, depth)
(rotation)
(class)
(confidence)

LiDAR-Based Detection

LiDAR naturally provides 3D geometry.

A typical pipeline is:

Point Cloud
    |
    v
Filtering
    |
    v
Voxelization / Features
    |
    v
Neural Network
    |
    v
3D Boxes

Challenges include sparse points and computational cost.

Camera-Based Detection

A camera provides dense visual information.

Monocular 3D detection tries to infer depth from a single image, while stereo systems can estimate depth geometrically.

Multi-Modal Detection

Combining cameras and LiDAR can provide both semantics and geometry:

Camera ---> Visual Features --+
                              |
LiDAR ----> 3D Features ------+--> Fusion --> 3D Detection

This is useful for autonomous robots operating around people, vehicles, and objects.

Post-Processing

Raw detections are often filtered using:

• Confidence thresholds
• Non-maximum suppression
• Geometric constraints
• Temporal tracking

Tracking can stabilize detections across frames.

ROS 2 Integration

A practical architecture:

/sensors/camera
/sensors/lidar
       |
       v
/3d_detector
       |
       v
/objects_3d
       |
       +--> /tracker
       |
       +--> /planner

Use standardized message structures where practical so perception remains decoupled from planning.

Measuring Performance

Evaluate:

• Precision
• Recall
• 3D IoU
• Position error
• Orientation error
• Inference latency
• FPS

For physical AI, latency matters almost as much as accuracy.

A detector that is accurate but too slow can still be unsuitable for a moving robot.

Production Considerations

Test across:

• Day/night conditions
• Different sensor placements
• Partial occlusion
• Different object sizes
• Dynamic scenes
• Sensor degradation

3D object detection is the bridge between perception and physical action: it transforms raw sensor data into spatially meaningful objects that a robot can reason about.

Useful Links

• Website: https://www.v-modal.com
• SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter
• SDK Android: https://github.com/v-modal/vmodal_sdk_android
• Discord: https://discord.gg/K72z28KU