A Cross-view Fusion Framework for Robust 6-DoF Grasp Pose Estimation
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 enhance 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...
| Authors | Kangjian Zhu, Haobo Jiang, Jianjun Qian et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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