CVPR 2021: Square Root Bundle Adjustment for Large-Scale Reconstruction

This is the CVPR 2021 presentation video for our work: Square Root Bundle Adjustment for Large-Scale Reconstruction Authors: Nikolaus Demmel, Christiane Sommer, Daniel Cremers and Vladyslav Usenko Paper: Project Page: Abstract: We propose a new formulation for the bundle adjustment problem which relies on nullspace marginalization of landmark variables by QR decomposition. Our approach, which we call square root bundle adjustment, is algebraically equivalent to the commonly used Schur complement trick, improves the numeric stability of computations, and allows for solving large-scale bundle adjustment problems with single-precision floating-point numbers. We show in real-world experiments with the BAL datasets that even in single precision the proposed solver achieves on average equally accurate solutions compared to Schur complement solvers using double precision. It runs significantly faster, but can require larger amounts of memory on