3D Reconstruction

3D reconstruction studies how to infer the shape and appearance of objects or scenes, along with camera positions and motion, from partial observations. Methods range from structure-from-motion and multi-view stereo to neural radiance fields, Gaussian splatting, signed distance fields, meshes, and feed-forward models that predict geometry directly; some also use diffusion priors, active view selection, or physical sensor models. Applications include mapping and robotics, digital twins, medical imaging, and human or object modeling, with evaluations often comparing both rendered views and geometric accuracy on indoor, outdoor, and specialized datasets. A central challenge is that realistic novel-view images do not always imply accurate, metrically consistent geometry, especially with sparse or unposed views, changing scenes, difficult materials, or limited computation.