Papers › Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
Siang Chen, Wei Tang, Pengwei Xie, Wenming Yang, Guijin Wang
Fast and robust object grasping in clutter is a crucial component of robotics. Most current works resort to the whole observed point cloud for 6-Dof grasp generation, ignoring the guidance information excavated from global semantics, thus limiting high-quality grasp generation and real-time performance. In this work, we show that the widely used heatmaps are underestimated in the efficiency of 6-Dof grasp generation. Therefore, we propose an effective local grasp generator combined with grasp heatmaps as guidance, which infers in a global-to-local semantic-to-point way. Specifically, Gaussian encoding and the grid-based strategy are applied to predict grasp heatmaps as guidance to aggregate local points into graspable regions and provide global semantic information. Further, a novel non-uniform anchor sampling mechanism is designed to improve grasp accuracy and diversity. Benefiting from the high-efficiency encoding in the image space and focusing on points in local graspable regions, our framework can perform high-quality grasp detection in real-time and achieve state-of-the-art results. In addition, real robot experiments demonstrate the effectiveness of our method with a success rate of 94% and a clutter completion rate of 100%. Our code is available at https://github.com/THU-VCLab/HGGD.
In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.
Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Robotic Grasping | GraspNet-1Billion | HGGD-CD | AP_novel | 24.59 | #5 of 8 | Archive leaderboard | report |
| Robotic Grasping | GraspNet-1Billion | HGGD-CD | AP_seen | 64.45 | #5 of 8 | Archive leaderboard | report |
| Robotic Grasping | GraspNet-1Billion | HGGD-CD | AP_similar | 53.59 | #5 of 8 | Archive leaderboard | report |
| Robotic Grasping | GraspNet-1Billion | HGGD-CD | mAP | 47.54 | #5 of 8 | Archive leaderboard | report |
| Robotic Grasping | GraspNet-1Billion | HGGD | AP_novel | 22.17 | #6 of 8 | Archive leaderboard | report |
| Robotic Grasping | GraspNet-1Billion | HGGD | AP_seen | 59.36 | #6 of 8 | Archive leaderboard | report |
| Robotic Grasping | GraspNet-1Billion | HGGD | AP_similar | 51.20 | #6 of 8 | Archive leaderboard | report |
| Robotic Grasping | GraspNet-1Billion | HGGD | mAP | 44.24 | #6 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections