A Cross-view Fusion Framework for Robust 6-DoF Grasp Pose Estimation

• URL: http://arxiv.org/abs/2606.06878v1
• Date: Fri, 05 Jun 2026 03:45:10 GMT
• Title: A Cross-view Fusion Framework for Robust 6-DoF Grasp Pose Estimation
• Abstract summary: Cross-view fusion framework enhances robustness of 6-DoF grasp pose estimation in corner views.<n>We propose a self-supervised contrastive learning strategy that leverages cross-view associations to regularize point cloud features.<n>Our framework achieves strong performance on the GraspNet-1Billion benchmark and in real-world applications.
• Score: 34.48829831555172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 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 https://github.com/KJZhuAutomatic/Cross-view-Grasp.

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