# 6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling

arXiv: https://arxiv.org/abs/2407.01209 · HTML: https://mlsanity.com/paper/2407.01209/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [A Segmented Robot Grasping Perception Neural Network for Edge AI](https://mlsanity.com/paper/2507.13970/) (2025)
- [GraRe: Grasp Candidate Re-Ranking for Frozen 6-DoF Grasp Detectors](https://mlsanity.com/paper/2608.00946/) (2026)
- [Towards Alias-Free 4D Gaussian Representations with Motion-Aware Filtering](https://mlsanity.com/paper/2608.21828/) (2026)

## Abstract (arXiv)

6-DoF grasp detection of small-scale grasps is crucial for robots to perform specific tasks. This paper focuses on enhancing the recognition capability of small-scale grasping, aiming to improve the overall accuracy of grasping prediction results and the generalization ability of the network. We propose an enhanced receptive field method that includes a multi-radii cylinder grouping module and a passive attention module. This method enhances the receptive field area within the graspable space and strengthens the learning of graspable features. Additionally, we design a graspable balance sampling module based on a segmentation network, which enables the network to focus on features of small objects, thereby improving the recognition capability of small-scale grasping. Our network achieves state-of-the-art performance on the GraspNet-1Billion dataset, with an overall improvement of approximately 10% in average precision@k (AP). Furthermore, we deployed our grasp detection model in pybullet grasping platform, which validates the effectiveness of our method.