Multigrid for Bundle Adjustment
Tristan Konolige, Jed Brown

TL;DR
This paper introduces a multigrid preconditioner for bundle adjustment that significantly improves the speed of solving large-scale problems, achieving up to 13 times faster performance than current methods.
Contribution
It proposes an unsmoothed aggregation multigrid preconditioner that better captures global modes, addressing superlinear scaling issues in large bundle adjustment problems.
Findings
Achieves up to 13x faster solutions on large problems.
Addresses superlinear scaling in existing bundle adjustment methods.
Demonstrates improved conditioning with multigrid preconditioning.
Abstract
Bundle adjustment is an important global optimization step in many structure from motion pipelines. Performance is dependent on the speed of the linear solver used to compute steps towards the optimum. For large problems, the current state of the art scales superlinearly with the number of cameras in the problem. We investigate the conditioning of global bundle adjustment problems as the number of images increases in different regimes and fundamental consequences in terms of superlinear scaling of the current state of the art methods. We present an unsmoothed aggregation multigrid preconditioner that accurately represents the global modes that underlie poor scaling of existing methods and demonstrate solves of up to 13 times faster than the state of the art on large, challenging problem sets.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Numerical Methods in Computational Mathematics · Robotics and Sensor-Based Localization
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
