并行刚性对光束法平差的重要性
Parallel Rigidity Matters for Bundle Adjustment
把并行刚性理论引入 BA 的二部图拓扑分析，给出解唯一性（避免子图独立缩放）的可判定条件与高效算法，可直接嵌入全局式 SfM 管线用于剔除错位相机与三维点。
原文依据 we examine the unique solvability of the 3D reconstruction problem using parallel rigidity theory
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论光束法平差是计算机视觉中的一个长期存在的问题，它从二维图像观测中求解相机参数与三维点坐标。尽管已有大量工作从各个方面展开研究，例如适配不同的相机模型与传感器，以及对优化问题求解的考量，本文处理的是一个基础而独特的问题，即其解的唯一性。具体而言，我们利用并行刚性理论考察三维重建问题的唯一可解性。我们设计了一种算法，以确保光束法平差中由相机—三维点关系构成的二部图的拓扑不会导致其子图中边的独立缩放。为处理通常规模很大的二部图，我们利用三维重建问题中的相机—相机关系来提升效率。我们展示了该分析对全局式运动恢复结构管线的益处。应用所提出的算法可显著得到更干净的重建结果，去除错位的相机与三维点。
英文原文摘要 1086 字符
Bundle adjustment is a long-standing problem in computer vision that solves for camera parameters and 3D point coordinates from 2D image observations. While there has been much work on various aspects, like adaptation to different camera models and sensors, and considerations for solving the optimization problem, in this paper, we deal with a fundamental and distinct question of the uniqueness of its solution. In particular, we examine the unique solvability of the 3D reconstruction problem using parallel rigidity theory. We design an algorithm to ensure that the topology of the bipartite graph formed by the camera-3D point relations in bundle adjustment does not result in independent scaling of the edges in its subgraphs. To tackle the generally large-sized bipartite graph, we leverage camera-camera relationships in 3D reconstruction problems for efficiency. We demonstrate the benefits of our analysis for a global structure-from-motion pipeline. Applying our proposed algorithm results in significantly cleaner reconstructions by removing misplaced cameras and 3D points.