Monocular 3D Reconstruction
Overview
Monocular 3D reconstruction turns images or video from one camera into estimates of shape, depth, camera motion, and sometimes changing objects or people. Methods range from structure-from-motion and SLAM to learned depth and point-map models, neural radiance fields, and 3D Gaussian splatting, often strengthened with geometric, semantic, or physical priors. Current work increasingly uses pretrained vision models and feed-forward systems for faster reconstruction, while specialized methods address people, hands, interactions, medical scenes, and long videos. A central challenge remains that one view does not uniquely determine 3D scale or hidden structure, especially when the camera and scene both move; accuracy, temporal consistency, generalization, and runtime can trade off.
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