无需特征匹配，用深度图联合优化实现多视角点云精准对齐
Correspondence-Free Multiview Point Cloud Registration via Depth-Guided Joint Optimisation
- 用深度图表示全局地图，通过非线性最小二乘法联合优化位姿与地图
- 在真实场景下精度超越现有方法，复杂环境表现更优
- 适合点云特征难提取的工业重建与移动扫描场景
多视角点云配准是构建全局一致三维模型的基础任务。现有方法通常依赖多点云间的特征提取与数据关联，但在复杂环境中难以获得全局最优解。本文提出一种新型无对应关系的多视角点云配准方法：将全局地图表示为深度图，利用原始深度信息构建非线性最小二乘优化问题，联合估计各点云位姿与全局地图。与依赖显式特征提取和数据关联的传统基于特征的束调整不同，本方法通过点云位姿与全局深度图的隐式关联，在优化过程中动态迭代修正关联关系。在多个真实数据集上的大量实验表明，该方法在准确性上优于当前最先进方法，尤其在特征提取与数据关联困难的复杂环境下表现突出。
原文摘要 · Abstract (English)
Multiview point cloud registration is a fundamental task for constructing globally consistent 3D models. Existing approaches typically rely on feature extraction and data association across multiple point clouds; however, these processes are challenging to obtain global optimal solution in complex environments. In this paper, we introduce a novel correspondence-free multiview point cloud registration method. Specifically, we represent the global map as a depth map and leverage raw depth information to formulate a non-linear least squares optimisation that jointly estimates poses of point clouds and the global map. Unlike traditional feature-based bundle adjustment methods, which rely on explicit feature extraction and data association, our method bypasses these challenges by associating multi-frame point clouds with a global depth map through their corresponding poses. This data association is implicitly incorporated and dynamically refined during the optimisation process. Extensive evaluations on real-world datasets demonstrate that our method outperforms state-of-the-art approaches in accuracy, particularly in challenging environments where feature extraction and data association are difficult.
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