发表机构
Colorado School of Mines; University of Texas at Dallas; U.S. Army Combat Capabilities Development Command, Army Research Laboratory（科罗拉多矿业大学; 德克萨斯大学达拉斯分校; 美国陆军作战能力发展司令部陆军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PARTE利用平面结构作为互补证据，通过新颖的平面上下文直方图描述符和两级匹配，结合置信度加权图进行联合离群点剔除，实现鲁棒的全局点云配准，在多个基准上取得最高成功率。
AI 中文摘要
当有限的重叠、重复的几何结构和传感器噪声产生以离群点为主的对应集时，全局点云配准仍然具有挑战性。平面区域对于传统的点描述符来说尤其困难，因此在匹配前通常被抑制或丢弃。我们提出了PARTE（平面辅助的鲁棒变换估计），一种全局配准方法，该方法反而将平面结构视为互补的配准证据。PARTE提取平面块，并使用我们新颖的平面上下文直方图（PCH）来表示它们，该描述符编码每个块周围的几何信息，而两级匹配过程则识别可靠的平面对应关系。候选点和平面对应关系在置信度加权的兼容性图中合并，以进行联合离群点剔除，随后进行刚体变换估计。当没有可用的平面对应关系时，PARTE自然退化为仅点配准。我们在跨越六个室内和室外基准（涵盖密集RGB-D和稀疏LiDAR测量）的8,097个配准对上评估了PARTE。评估表明，与13种标准和最先进的方法相比，PARTE实现了最高的总体成功率，同时保持了较低的运行时间。在此https URL处提供了带有Python绑定的开源C++实现。
英文摘要
Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated by outliers. Planar regions are particularly difficult for conventional point descriptors and are therefore often suppressed or discarded before matching. We present PARTE (Plane-Assisted Robust Transformation Estimation), a global registration method that instead treats planar structure as complementary registration evidence. PARTE extracts planar patches and represents them using our novel Plane Context Histogram (PCH), a descriptor that encodes the geometry surrounding each patch, while a two-level matching procedure identifies reliable plane correspondences. Candidate point and plane correspondences are combined in a confidence-weighted compatibility graph for joint outlier rejection, followed by rigid transformation estimation. When no usable plane correspondences are available, PARTE naturally reduces to point-only registration. We evaluate PARTE on 8,097 registration pairs across six indoor and outdoor benchmarks spanning dense RGB-D and sparse LiDAR measurements. Evaluations show PARTE achieves the highest overall success rate against 13 standard and state-of-the-art methods while maintaining low runtime. An open-source C++ implementation with Python bindings is provided at https://ariarobotics.github.io/parte/.
Comments16 pages, 14 figures, 11 tables. Code: https://ariarobotics.github.io/parte/. Updated project website URL
