# Volumetric-based Contact Point Detection for 7-DoF Grasping

arXiv: https://arxiv.org/abs/2209.06675 · HTML: https://mlsanity.com/paper/2209.06675/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2022.

## Built on by (most cited first, 5 of 5)

- [SuperDec: 3D Scene Decomposition with Superquadric Primitives](https://mlsanity.com/paper/2504.00992/) (2025)
- [Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering](https://mlsanity.com/paper/2306.07392/) (2023)
- [Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies](https://mlsanity.com/paper/2211.10957/) (2022)
- [You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects](https://mlsanity.com/paper/2404.03462/) (2024)
- [GP-net: Flexible Viewpoint Grasp Proposal](https://mlsanity.com/paper/2209.10404/) (2022)

## Abstract (arXiv)

In this paper, we propose a novel grasp pipeline based on contact point detection on the truncated signed distance function (TSDF) volume to achieve closed-loop 7-degree-of-freedom (7-DoF) grasping on cluttered environments. The key aspects of our method are that 1) the proposed pipeline exploits the TSDF volume in terms of multi-view fusion, contact-point sampling and evaluation, and collision checking, which provides reliable and collision-free 7-DoF gripper poses with real-time performance; 2) the contact-based pose representation effectively eliminates the ambiguity introduced by the normal-based methods, which provides a more precise and flexible solution. Extensive simulated and real-robot experiments demonstrate that the proposed pipeline can select more antipodal and stable grasp poses and outperforms normal-based baselines in terms of the grasp success rate in both simulated and physical scenarios.