提出视觉视角约束，提升点云配准鲁棒性
Sight View Constraint for Robust Point Cloud Registration
- 引入视线视角约束判断变换是否错误
- 在3DLoMatch上召回率从78%提升至82%
- 适合处理重叠率低的点云配准问题
部分到部分点云配准（partial PCR）在重叠率较低时仍具挑战性。与全对全配准相比，我们发现部分PCR的目标尚未明确定义，即缺乏可靠指标识别真实变换。本文将此视为核心难题。为此，不直接寻找最优变换，而是提出一种新颖通用的视线视角约束（SVC），可明确排除错误变换，从而增强现有PCR方法的鲁棒性。大量实验验证了SVC在室内与室外场景的有效性。在具有挑战性的3DLoMatch数据集上，本方法将配准召回率从78%提升至82%，达到当前最优水平。研究还强调了部分PCR的决策版本问题的重要性，或能为该问题提供新视角。
原文摘要 · Abstract (English)
Partial to Partial Point Cloud Registration (partial PCR) remains a challenging task, particularly when dealing with a low overlap rate. In comparison to the full-to-full registration task, we find that the objective of partial PCR is still not well-defined, indicating no metric can reliably identify the true transformation. We identify this as the most fundamental challenge in partial PCR tasks. In this paper, instead of directly seeking the optimal transformation, we propose a novel and general Sight View Constraint (SVC) to conclusively identify incorrect transformations, thereby enhancing the robustness of existing PCR methods. Extensive experiments validate the effectiveness of SVC on both indoor and outdoor scenes. On the challenging 3DLoMatch dataset, our approach increases the registration recall from 78\% to 82\%, achieving the state-of-the-art result. This research also highlights the significance of the decision version problem of partial PCR, which has the potential to provide novel insights into the partial PCR problem.
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