用深度学习解决低重叠点云配准的评估难题，提升配准准确率。
Decision PCR: Decision version of the Point Cloud Registration task
- 构建基于3DMatch的数据集，训练深度分类器评估配准质量。
- 在3DLoMatch上使GeoTransformer召回率达86.97%，刷新SOTA纪录。
- 方法可通用集成到主流配准流程，适用于未见场景如ETH户外数据。
低重叠点云配准（PCR）仍是三维视觉中的重大挑战。传统评估指标（如最大内点数）在极低内点比下失效。本文重新审视配准结果评估问题，提出将决策版PCR作为核心任务。为此，我们提出一种数据驱动方法：首先基于3DMatch构建对应数据集；然后训练深度学习分类器，可靠评估配准质量，突破传统指标局限。据我们所知，这是首个通过深度学习框架解决该任务的全面研究。将该分类器融入标准PCR流程后，现有最先进方法性能显著提升。例如，与GeoTransformer结合，在具有挑战性的3DLoMatch基准上实现86.97%的新SOTA召回率。该方法在未见的室外ETH数据集上也表现出强泛化能力。
原文摘要 · Abstract (English)
Low-overlap point cloud registration (PCR) remains a significant challenge in 3D vision. Traditional evaluation metrics, such as Maximum Inlier Count, become ineffective under extremely low inlier ratios. In this paper, we revisit the registration result evaluation problem and identify the Decision version of the PCR task as the fundamental problem. To address this Decision PCR task, we propose a data-driven approach. First, we construct a corresponding dataset based on the 3DMatch dataset. Then, a deep learning-based classifier is trained to reliably assess registration quality, overcoming the limitations of traditional metrics. To our knowledge, this is the first comprehensive study to address this task through a deep learning framework. We incorporate this classifier into standard PCR pipelines. When integrated with our approach, existing state-of-the-art PCR methods exhibit significantly enhanced registration performance. For example, combining our framework with GeoTransformer achieves a new SOTA registration recall of 86.97\% on the challenging 3DLoMatch benchmark. Our method also demonstrates strong generalization capabilities on the unseen outdoor ETH dataset.
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