Square Root Bundle Adjustment for Large-Scale Reconstruction
Contact: Publications, Christiane Sommer
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 dense problems. The proposed formulation relies on simple linear algebra operations and opens the way for efficient implementations of bundle adjustment on hardware platforms optimized for single-precision linear algebra processing.
Poster
Open-Source Code
The code is available on https://github.com/NikolausDemmel/rootba
Related Publications
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Conference and Workshop Papers
2021
[] Square Root Marginalization for Sliding-Window Bundle Adjustment , In IEEE International Conference on Computer Vision (ICCV), 2021. ([project page])
[] Square Root Bundle Adjustment for Large-Scale Reconstruction , In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([project page])