Multi-View Geometry

Multi-view geometry studies how to infer camera relationships and three-dimensional structure from images captured at different viewpoints, enabling mapping, measurement, tracking, and reconstruction. Classical methods use feature correspondences, epipolar constraints, homographies, triangulation, and bundle adjustment, while learned matchers and geometry-aware transformers extend these tools to difficult images and larger view sets. Current work combines explicit geometric constraints with learned depth and foundation-model features across settings such as wide-baseline, uncalibrated, fisheye, aerial, and satellite imagery. A central challenge is making estimates reliable under weak overlap, noise, motion, and calibration errors while keeping methods efficient and evaluating accuracy in ways that detect coherent geometric failures.