R3PM-Net：实时鲁棒现实场景点云匹配网络

R3PM-Net: Real-time, Robust, Real-world Point Matching Network

April 6, 2026

作者: Yasaman Kashefbahrami, Erkut Akdag, Panagiotis Meletis, Evgeniya Balmashnova, Dip Goswami, Egor Bondarau

cs.AI

摘要

精确点云配准（PCR）是三维数据处理中的重要任务，涉及两个点云间刚性变换的估计。尽管深度学习方法解决了传统非学习方法对噪声、异常值、遮挡和初始化的敏感性问题，但这些方法均在洁净、稠密的合成数据集上开发评估（限制了其在真实工业场景中的泛化能力）。本文提出R3PM-Net——一种轻量化、全局感知的物体级点匹配网络，通过兼顾泛化性与实时效率来弥合这一差距。为支持此转型，我们构建了Sioux-Cranfield和Sioux-Scans两个数据集，为不完美摄影测量/事件相机扫描数据与数字CAD模型的配准提供评估基准，并已公开共享。大量实验表明，R3PM-Net在保持竞争力的精度下实现了无与伦比的速度：在ModelNet40上仅用0.007秒即达到1的完美匹配度与0.029厘米的内点RMSE，比最先进方法RegTR快约7倍；该性能在Sioux-Cranfield数据集上得以延续，保持1的匹配度与0.030厘米内点RMSE，且延迟同样极低；在极具挑战性的Sioux-Scans数据集上，R3PM-Net能在50毫秒内成功解决边缘案例。这些结果证实了R3PM-Net为精度与实时性至关重要的工业应用提供了鲁棒的高速解决方案。代码与数据集详见https://github.com/YasiiKB/R3PM-Net。

English

Accurate Point Cloud Registration (PCR) is an important task in 3D data processing, involving the estimation of a rigid transformation between two point clouds. While deep-learning methods have addressed key limitations of traditional non-learning approaches, such as sensitivity to noise, outliers, occlusion, and initialization, they are developed and evaluated on clean, dense, synthetic datasets (limiting their generalizability to real-world industrial scenarios). This paper introduces R3PM-Net, a lightweight, global-aware, object-level point matching network designed to bridge this gap by prioritizing both generalizability and real-time efficiency. To support this transition, two datasets, Sioux-Cranfield and Sioux-Scans, are proposed. They provide an evaluation ground for registering imperfect photogrammetric and event-camera scans to digital CAD models, and have been made publicly available. Extensive experiments demonstrate that R3PM-Net achieves competitive accuracy with unmatched speed. On ModelNet40, it reaches a perfect fitness score of 1 and inlier RMSE of 0.029 cm in only 0.007s, approximately 7 times faster than the state-of-the-art method RegTR. This performance carries over to the Sioux-Cranfield dataset, maintaining a fitness of 1 and inlier RMSE of 0.030 cm with similarly low latency. Furthermore, on the highly challenging Sioux-Scans dataset, R3PM-Net successfully resolves edge cases in under 50 ms. These results confirm that R3PM-Net offers a robust, high-speed solution for critical industrial applications, where precision and real-time performance are indispensable. The code and datasets are available at https://github.com/YasiiKB/R3PM-Net.