Matrix-Free Shared Intrinsics Bundle Adjustment
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.
Cite
Text
Safari. "Matrix-Free Shared Intrinsics Bundle Adjustment." Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.02516Markdown
[Safari. "Matrix-Free Shared Intrinsics Bundle Adjustment." Conference on Computer Vision and Pattern Recognition, 2025.](https://mlanthology.org/cvpr/2025/safari2025cvpr-matrixfree/) doi:10.1109/CVPR52734.2025.02516BibTeX
@inproceedings{safari2025cvpr-matrixfree,
title = {{Matrix-Free Shared Intrinsics Bundle Adjustment}},
author = {Safari, Daniel},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2025},
pages = {27017-27026},
doi = {10.1109/CVPR52734.2025.02516},
url = {https://mlanthology.org/cvpr/2025/safari2025cvpr-matrixfree/}
}