SuP：子点云驱动的点云配准
SuP: Sub-cloud Driven Point Cloud Registration
把低重叠配准转化为高重叠子点云对（锚框对）挖掘问题，用特征显著性先验加权加多尺度邻域一致性后加权筛选锚框对，再做 merge-to-match 生成对应关系；对做点云配准、部分重叠匹配的人可直接复用这套子点云锚框挖掘与对齐感知加权损失。
原文依据 reformulates low-overlap registration as a high-overlap sub-cloud pairs (anchor pairs) mining problem
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论现有点云配准方法能够很好地处理两个点云的高重叠场景，但由于非重叠区域中不可避免的几何/语义歧义，它们在低重叠场景下往往表现不佳。本文提出 SuP，一种新颖的框架，将低重叠配准重新表述为高重叠子点云对（锚框对）挖掘问题。SuP 的核心是双阶段子点云锚框挖掘（DSAM）模块，该模块首先将源点云与目标点云细分为多个子点云，随后引入双阶段加权流程：1）一种高效的、由重叠引导的先验加权方案（OPS），利用特征显著性识别候选锚框对；2）一个多尺度后加权网络（MPN），利用邻域特征一致性进一步识别锚框对。随后，通过一个 merge-to-match 模块利用这些锚框对生成最终对应关系。为训练 DSAM，我们设计了一种对齐感知加权损失，以在线计算的对齐误差作为监督信号。在色彩增强的 3DMatch 与 3DLoMatch 上的大量实验表明，SuP 显著优于最优方法，取得了更高的配准召回率与更精确的对齐，尤其是在具有挑战性的低重叠条件下。
英文原文摘要 1346 字符
While existing point-cloud-registration methods can well handle high-overlap scenarios of two point clouds, they often struggle with low-overlap scenarios, due to inevitable geometric/semantic ambiguities in the non-overlapping regions. In this paper, we introduce SuP, a novel framework that reformulates low-overlap registration as a high-overlap sub-cloud pairs (anchor pairs) mining problem. Central to SuP is our Dual-phase Sub-cloud Anchor Mining (DSAM) module, which first subdivides the source and target point clouds into multiple sub-clouds, followed by introducing a dual-phase weighting pipeline: 1) an efficient overlap-guided prior-weighting scheme (OPS) that leverages feature salience to identify candidate anchor pairs, and 2) a multi-scale post-weighting network (MPN) that exploits neighborhood feature consensus to further identify anchor pairs. Subsequently, final correspondences are generated through a merge-to-match module using the anchor pairs. To train DSAM, we design an alignment-aware weighting loss that uses on-the-fly alignment errors as supervision. Comprehensive experiments on the color-enhanced 3DMatch and 3DLoMatch demonstrate that SuP significantly outperforms state-of-the-art methods, achieving higher registration recall and more accurate alignment, especially under challenging low-overlap conditions.