Generalized-CVO：基于二阶黎曼优化的快速无对应局部点云配准
Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
提出一种基于RKHS嵌入和二阶黎曼优化的无对应点云配准方法，可显著提升LiDAR/RGB-D帧间跟踪精度与速度，适用于需要快速局部配准的视觉里程计或跟踪系统。
原文依据 We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出了一种快速且无对应的局部点云配准方法，该方法利用几何表面结构和再生核希尔伯特空间（RKHS）嵌入。该方法将点云表示为具有逐点各向异性核的连续函数，这些核编码了局部几何。这种形式改善了沿表面法线的对齐，同时放宽了沿切线方向的对齐。为了求解所得的配准问题，我们提出了一种基于近似黎曼Hessian的流形上二阶优化方案，相比先前基于RKHS的无对应方法中使用的一阶求解器，实现了高达10倍的加速。我们在多样的室内外数据集上展示了改进的帧间LiDAR和RGB-D跟踪精度。在驾驶领域的LiDAR跟踪配准任务中，我们在具有挑战性的特征稀疏环境下，平移和旋转漂移均减少了超过55%。在物体配准基准上，我们展示了相比基于ICP的方法更好的鲁棒性，并且在优化全局初始化时进一步获益，尤其是在中等程度错位的情况下。
英文原文摘要 1149 字符
We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings. The method represents point clouds as continuous functions with point-wise anisotropic kernels that encode local geometry. This formulation improves alignment along surface normals while relaxing alignment along tangential directions. To solve the resulting registration problem, we propose a second-order on-manifold optimization scheme with approximate Riemannian Hessians, achieving a speedup of up to 10x over the first-order solvers used in prior correspondence-free RKHS-based methods. We demonstrate improved frame-to-frame LiDAR and RGB-D tracking accuracy across diverse indoor and outdoor datasets. On a LiDAR tracking registration task in the driving domain, we achieve a reduction of >55% in both translational and rotational drift in challenging feature-sparse environments. On object registration benchmarks, we show improved robustness over ICP-based methods and further gains when refining global initialization, particularly under moderate misalignment.