决策PCR：点云配准任务的决策版本
Decision PCR: Decision version of the Point Cloud Registration task
AI总结：
本文提出决策PCR任务，通过基于3DMatch构建数据集并训练深度学习分类器评估配准质量，集成到现有PCR流程后显著提升性能，与GeoTransformer结合在3DLoMatch上达到86.97%的新SOTA召回率。
摘要：
低重叠点云配准（PCR）仍然是三维视觉中的一个重大挑战。传统的评估指标，如最大内点数量，在极低内点比例下变得无效。在本文中，我们重新审视了配准结果评估问题，并将PCR任务的决策版本确定为基础性问题。为了解决这一决策PCR任务，我们提出了一种数据驱动的方法。首先，我们基于3DMatch数据集构建了一个相应的数据集。然后，训练一个基于深度学习的分类器来可靠地评估配准质量，克服了传统指标的局限性。据我们所知，这是首个通过深度学习框架全面研究该任务的工作。我们将该分类器集成到标准PCR流程中。当与我们的方法结合时，现有的最先进PCR方法展现出显著增强的配准性能。例如，将我们的框架与GeoTransformer结合，在具有挑战性的3DLoMatch基准上实现了86.97%的新SOTA配准召回率。我们的方法在未见过的室外ETH数据集上也表现出强大的泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
