Perception

3D Reconstruction

3D reconstruction is the recovery of the three-dimensional geometry, and often appearance, of scenes or objects from sensor data such as multiple images, depth maps, or LiDAR scans. Classical pipelines combine structure-from-motion, multi-view stereo, and TSDF fusion into meshes, while neural approaches such as NeRF and 3D Gaussian splatting represent scenes as optimizable radiance fields with photorealistic novel-view synthesis.

Why it matters for physical AI

Reconstructed geometry feeds grasp planning, collision checking, and digital twins, and fast photorealistic reconstruction increasingly closes the real-to-sim loop, turning captures of real workspaces into training environments for policies.