Bundle Adjustment is a nonlinear least-squares problem that moves many camera poses and 3D points at once to minimize the reprojection error across all observations. This article systematically covers why the Jacobian is sparse, why the Schur complement makes large-scale problems solvable, the role of the Levenberg-Marquardt method, gauge freedom, and the relationship to Pose Graph optimization and SfM, with equations and diagrams.
September 4, 2026 Computer Vision PrimerStructure from Motion (SfM) recovers both a scene's 3D structure and every camera pose simultaneously from a set of photos whose order and shooting positions are unknown. This article systematically covers feature matching, geometric verification, the two reconstruction strategies of Incremental and Global SfM, triangulation, the relationship to Bundle Adjustment, and the difference from Visual-SLAM, alongside current implementations like COLMAP.