Bundle Adjustment
Overview
Bundle adjustment estimates camera poses and scene structure together, usually by minimizing reprojection errors across many images; it underpins structure-from-motion, visual SLAM, and camera calibration. Classical solvers use sparse nonlinear least squares, often with Gauss–Newton or Levenberg–Marquardt updates and Schur-complement elimination, while newer systems exploit GPUs or square-root formulations for speed. Methods also incorporate robust losses, depth and inertial priors, multi-sensor constraints, or learned image matching, and extend the optimized structure beyond points to lines and other geometric groups. A central challenge is achieving fast, stable optimization when initialization is poor or geometry is weak, without sacrificing accuracy or global consistency as datasets grow.
Latest papers
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