Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment
<|reference_start|>Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment: Most Bundle Adjustment (BA) solvers like the Levenberg-Marquardt algorithm require a good initialization. Instead, initialization-free BA remains a largely uncharted territory. The under-explored Variable Projection algorithm (VarPro) exhibits a wide convergence basin even without initialization. Coupled with object space error formulation, recent works have shown its ability to solve small-scale initialization-free bundle adjustment problem. To make such initialization-free BA approaches scalable, we introduce Power Variable Projection (PoVar), extending a recent inverse expansion method based on power series. Importantly, we link the power series expansion to Riemannian manifold optimization. This projective framework is crucial to solve large-scale bundle adjustment problems without initialization. Using the real-world BAL dataset, we experimentally demonstrate that our solver achieves state-of-the-art results in terms of speed and accuracy. To our knowledge, this work is the first to address the scalability of BA without initialization opening new venues for initialization-free structure-from-motion.<|reference_end|>
@article{weber2024power,
    title={Power Variable Projection for Initialization-Free Large-Scale Bundle
  Adjustment},
    author={Simon Weber and Je Hyeong Hong and Daniel Cremers},
    journal={arXiv preprint arXiv:2405.05079},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05079},
    primaryClass={cs.CV}
}