Matrix-Free Shared Intrinsics Bundle Adjustment

Daniel Safari; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 27017-27026

Abstract


Research on accelerating bundle adjustment has focused on photo collections where each image is accompanied by its own set of camera parameters. However, real-world applications overwhelmingly call for shared intrinsics bundle adjustment (SI-BA) where camera parameters are shared across multiple images. Utilizing overlooked optimization opportunities specific to SI-BA, most notably matrix-free computation, we present a solver that is eight times faster than alternatives while consuming a tenth of the memory. Additionally, we examine factors contributing to BA instability under single-precision computation and propose mitigations.

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[bibtex]
@InProceedings{Safari_2025_CVPR, author = {Safari, Daniel}, title = {Matrix-Free Shared Intrinsics Bundle Adjustment}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, month = {June}, year = {2025}, pages = {27017-27026} }