提出混合运动注册方法，提升动态场景点云配准鲁棒性
HybridReg: Robust 3D Point Cloud Registration with Hybrid Motions
- 通过学习不确定性掩码区分背景刚性与前景非刚性运动
- 在复杂场景中实现优于现有方法的配准精度（尤其在动态物体干扰下）
- 适合需要处理真实世界动态场景的3D重建与机器人导航任务
当存在动态前景时，场景级点云配准极具挑战。现有室内数据集多假设刚性运动，导致模型难以应对非刚性运动；而现有的非刚性数据集主要为物体级，模型难以泛化到复杂场景。本文提出 HybridReg，一种新型3D点云配准方法，通过学习不确定性掩码来建模混合运动：背景为刚性，前景为实例级非刚性或刚性。首先，构建名为 HybridMatch 的场景级点云配准数据集，通过可控策略布置多样变形前景。其次，设计掩码学习模块以缓解变形异常值的干扰。第三，采用简单有效的负对数似然损失，利用不确定性引导特征提取与相关性计算。据我们所知，HybridReg 是首个针对混合运动进行鲁棒点云配准的工作。大量实验表明，该方法在广泛使用的室内外数据集上均达到领先性能。
原文摘要 · Abstract (English)
Scene-level point cloud registration is very challenging when considering dynamic foregrounds. Existing indoor datasets mostly assume rigid motions, so the trained models cannot robustly handle scenes with non-rigid motions. On the other hand, non-rigid datasets are mainly object-level, so the trained models cannot generalize well to complex scenes. This paper presents HybridReg, a new approach to 3D point cloud registration, learning uncertainty mask to account for hybrid motions: rigid for backgrounds and non-rigid/rigid for instance-level foregrounds. First, we build a scene-level 3D registration dataset, namely HybridMatch, designed specifically with strategies to arrange diverse deforming foregrounds in a controllable manner. Second, we account for different motion types and formulate a mask-learning module to alleviate the interference of deforming outliers. Third, we exploit a simple yet effective negative log-likelihood loss to adopt uncertainty to guide the feature extraction and correlation computation. To our best knowledge, HybridReg is the first work that exploits hybrid motions for robust point cloud registration. Extensive experiments show HybridReg's strengths, leading it to achieve state-of-the-art performance on both widely-used indoor and outdoor datasets.
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