Perception
Point Cloud
A point cloud is a set of 3D points, optionally carrying color, intensity, or normals, produced by LiDAR, RGB-D cameras, stereo matching, or structure-from-motion. Point clouds are unordered and vary in density with distance and viewpoint, so processing them requires permutation-invariant methods, from classical voxel grids and normal estimation to learned architectures such as PointNet and sparse 3D convolutions.
Why it matters for physical AI
Point clouds are the most direct geometric input for grasp synthesis, obstacle avoidance, and mapping, and point-cloud-conditioned policies can generalize across visual appearance changes that defeat RGB-only models.
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