A robot needs more than image classification.
It needs to know:
3D object detection answers these questions in physical space.
Camera / LiDAR
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v
Preprocessing
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v
Feature Extraction
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v
3D Detection Model
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v
3D Bounding Boxes
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v
Tracking / Planning
A 3D bounding box can contain:
(x, y, z)
(width, height, depth)
(rotation)
(class)
(confidence)
LiDAR naturally provides 3D geometry.
A typical pipeline is:
Point Cloud
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v
Filtering
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v
Voxelization / Features
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v
Neural Network
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v
3D Boxes
Challenges include sparse points and computational cost.
A camera provides dense visual information.
Monocular 3D detection tries to infer depth from a single image, while stereo systems can estimate depth geometrically.
Combining cameras and LiDAR can provide both semantics and geometry:
Camera ---> Visual Features --+
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LiDAR ----> 3D Features ------+--> Fusion --> 3D Detection
This is useful for autonomous robots operating around people, vehicles, and objects.
Raw detections are often filtered using:
Tracking can stabilize detections across frames.
A practical architecture:
/sensors/camera
/sensors/lidar
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v
/3d_detector
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v
/objects_3d
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+--> /tracker
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+--> /planner
Use standardized message structures where practical so perception remains decoupled from planning.
Evaluate:
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.
Test across:
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.