广义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
AI总结：
提出一种基于几何表面结构和再生核希尔伯特空间嵌入的无对应局部点云配准方法，采用二阶流形优化实现高达10倍加速，在LiDAR和RGB-D跟踪及物体配准中显著降低漂移并提升鲁棒性。
摘要：
我们提出了一种快速且无需对应关系的局部点云配准方法，该方法利用了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信息。这种公式化在沿表面法线方向改善对齐的同时，放松了沿切线方向的对齐。为了解决由此产生的配准问题，我们提出了一种具有近似黎曼海森矩阵的二阶流形优化方案，与先前基于无对应RKHS方法中使用的一阶求解器相比，实现了高达10倍的加速。我们展示了在多种室内外数据集上改进的帧到帧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实现了平移和旋转漂移均减少超过55%。在物体配准基准测试中，我们展示了相比基于ICP的方法更强的鲁棒性，并且在优化全局初始化时（尤其是在中等错位情况下）获得了进一步的提升。
英文摘要：
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.
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