用几何注意力提升点云低重叠时的配准精度
Fully-Geometric Cross-Attention for Point Cloud Registration
- 在超点级融合坐标与特征，实现旋转平移不变的跨云注意力
- 引入格罗莫夫-瓦瑟斯坦距离，提升低重叠场景下的匹配准确率
- 适合点云配准、3D重建等需要高鲁棒性的场景
点云配准方法在重叠度较低时常因噪声对应关系而失效。本文提出一种专为Transformer架构设计的新式交叉注意力机制，通过在点云间的超点层级融合坐标与特征信息来解决此问题。该方法需保证旋转和平移不变性，因点云处于独立参考系中。我们引入格罗莫夫-瓦瑟斯坦距离，联合计算不同点云间点对的距离并考虑其几何结构。由此，两个不同点云中的点可在任意刚性变换下相互关注。在点层级，还设计了自注意力机制，将局部几何结构信息聚合到点特征中以实现精细匹配。该方法显著提升了内点对应数量，相比现有最优方法获得更精确的配准结果。我们在3DMatch、3DLoMatch、KITTI和3DCSR数据集上进行了广泛评估。
原文摘要 · Abstract (English)
Point cloud registration approaches often fail when the overlap between point clouds is low due to noisy point correspondences. This work introduces a novel cross-attention mechanism tailored for Transformer-based architectures that tackles this problem, by fusing information from coordinates and features at the super-point level between point clouds. This formulation has remained unexplored primarily because it must guarantee rotation and translation invariance since point clouds reside in different and independent reference frames. We integrate the Gromov-Wasserstein distance into the cross-attention formulation to jointly compute distances between points across different point clouds and account for their geometric structure. By doing so, points from two distinct point clouds can attend to each other under arbitrary rigid transformations. At the point level, we also devise a self-attention mechanism that aggregates the local geometric structure information into point features for fine matching. Our formulation boosts the number of inlier correspondences, thereby yielding more precise registration results compared to state-of-the-art approaches. We have conducted an extensive evaluation on 3DMatch, 3DLoMatch, KITTI, and 3DCSR datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。