Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment
arxiv(2024)
摘要
Initialization-free bundle adjustment (BA) remains largely uncharted. While
Levenberg-Marquardt algorithm is the golden method to solve the BA problem, it
generally relies on a good initialization. In contrast, 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. 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 problem 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. In particular, our work is the first, to our knowledge, that
addresses the scalability of BA without initialization and opens new venues for
initialization-free Structure-from-Motion.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要