面向鲁棒 6-DoF 抓取姿态估计的跨视角融合框架
A Cross-view Fusion Framework for Robust 6-DoF Grasp Pose Estimation
用自监督对比学习把跨视角点云特征在空间一致性和抓取方向区分性上对齐，再以圆柱坐标系配准+局部自注意力/种子交叉注意力交替融合，为多视角点云融合与遮挡鲁棒的位姿估计提供了可复用的后融合范式。
原文依据 we propose a self-supervised contrastive learning strategy that leverages cross-view associations to regularize point cloud features
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出一种跨视角融合框架，用于增强 6-DoF 抓取姿态估计在角落视角下的鲁棒性。该框架通过引入辅助视角缓解遮挡问题，并借助后融合策略避免耗时且与任务无关的多视角重建。为实现跨视角融合，我们提出一种自监督对比学习策略，利用跨视角关联对点云特征进行正则化。简言之，若两个点对应同一三维位置，则跨视角点对被视为匹配；若二者代表不同的抓取方向，则视为不匹配。该学习策略显著增强了点特征的空间一致性与方向区分性，从而促进跨视角融合并提升估计鲁棒性。此外，我们提出一种跨视角对齐的圆柱积分模块，将抓取相关几何融合为综合表示。具体而言，该模块首先依据相似度对齐跨视角的点与特征，以增强对噪声的鲁棒性；随后将这些点配准到圆柱坐标系中，突出对抓取十分重要的旋转对称几何；最后交替使用局部自注意力层与种子交叉注意力层，分别实现单视角内部与跨视角之间的交互，从而支持抓取相关几何的细粒度表示。我们的框架在 GraspNet-1Billion 基准和真实世界应用中均取得强劲性能。代码已在 GitHub 上公开。
英文原文摘要 1638 字符
In this paper, we propose a cross-view fusion framework that enhances the robustness of 6-DoF grasp pose estimation in corner views.Our framework alleviates occlusion by incorporating an auxiliary view and avoids the time-consuming, task-agnostic multi-view reconstruction through a post-fusion strategy.To enable cross-view fusion, we propose a self-supervised contrastive learning strategy that leverages cross-view associations to regularize point cloud features.In brief, a cross-view point pair is considered a match if the two points correspond to the same 3D location, and a non-match if they represent distinct grasp directions.The learning strategy significantly enhances the spatial consistency and direction distinctiveness of point features, thereby facilitating cross-view fusion and improving estimation robustness.Furthermore, we propose a cross-view-aligned cylinder integration module to fuse grasp-relevant geometry into a comprehensive representation.Specifically, the module first aligns the cross-view points and features according to their similarity to enhance the robustness against noise.Subsequently, these points are registered into the cylindrical coordinate frame, emphasizing the rotation-symmetric geometry which is important for grasping.Finally, local self-attention and seed cross-attention layers are alternately employed, respectively enabling interactions within single views and across views, which supports fine-grained representation of grasp-relevant geometry.Our framework achieves strong performance on the GraspNet-1Billion benchmark and in real-world applications. Code is available at GitHub.