# PCF-Grasp: Converting Point Completion to Geometry Feature to Enhance 6-DoF Grasp

arXiv: https://arxiv.org/abs/2504.16320 · HTML: https://mlsanity.com/paper/2504.16320/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 49 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 30 of 32)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion](https://mlsanity.com/paper/1901.04780/) (2019)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://mlsanity.com/paper/2006.03761/) (2020)
- [ACRONYM: A Large-Scale Grasp Dataset Based on Simulation](https://mlsanity.com/paper/2011.09584/) (2020)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Grasp Pose Detection in Point Clouds](https://mlsanity.com/paper/1706.09911/) (2017)
- [PointNetGPD: Detecting Grasp Configurations from Point Sets](https://mlsanity.com/paper/1809.06267/) (2018)
- [Shape Completion Enabled Robotic Grasping](https://mlsanity.com/paper/1609.08546/) (2016)
- [6-DOF Grasping for Target-driven Object Manipulation in Clutter](https://mlsanity.com/paper/1912.03628/) (2019)
- [Learning to Reconstruct Shapes from Unseen Classes](https://mlsanity.com/paper/1812.11166/) (2018)
- [Graspness Discovery in Clutters for Fast and Accurate Grasp Detection](https://mlsanity.com/paper/2406.11142/) (2024)
- [kPAM-SC: Generalizable Manipulation Planning using KeyPoint Affordance and Shape Completion](https://mlsanity.com/paper/1909.06980/) (2019)
- [Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/2212.05275/) (2022)
- [Robust Grasp Planning Over Uncertain Shape Completions](https://mlsanity.com/paper/1903.00645/) (2019)
- [Learning Local Displacements for Point Cloud Completion](https://mlsanity.com/paper/2203.16600/) (2022)
- [3DSGrasp: 3D Shape-Completion for Robotic Grasp](https://mlsanity.com/paper/2301.00866/) (2023)
- [Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/2403.18546/) (2024)
- [Beyond Top-Grasps Through Scene Completion](https://mlsanity.com/paper/1909.12908/) (2019)
- [Simultaneous Object Reconstruction and Grasp Prediction using a Camera-centric Object Shell Representation](https://mlsanity.com/paper/2109.06837/) (2021)
- [TransSC: Transformer-based Shape Completion for Grasp Evaluation](https://mlsanity.com/paper/2107.00511/) (2021)

## Built on by (most cited first, 1 of 1)

- [Rethinking Transparent Object Grasping: Depth Completion with Monocular Depth Estimation and Instance Mask](https://mlsanity.com/paper/2508.02507/) (2025)

## Abstract (arXiv)

The 6-Degree of Freedom (DoF) grasp method based on point clouds has shown significant potential in enabling robots to grasp target objects. However, most existing methods are based on the point clouds (2.5D points) generated from single-view depth images. These point clouds only have one surface side of the object providing incomplete geometry information, which mislead the grasping algorithm to judge the shape of the target object, resulting in low grasping accuracy. Humans can accurately grasp objects from a single view by leveraging their geometry experience to estimate object shapes. Inspired by humans, we propose a novel 6-DoF grasping framework that converts the point completion results as object shape features to train the 6-DoF grasp network. Here, point completion can generate approximate complete points from the 2.5D points similar to the human geometry experience, and converting it as shape features is the way to utilize it to improve grasp efficiency. Furthermore, due to the gap between the network generation and actual execution, we integrate a score filter into our framework to select more executable grasp proposals for the real robot. This enables our method to maintain a high grasp quality in any camera viewpoint. Extensive experiments demonstrate that utilizing complete point features enables the generation of significantly more accurate grasp proposals and the inclusion of a score filter greatly enhances the credibility of real-world robot grasping. Our method achieves a 17.8% success rate higher than the state-of-the-art method in real-world experiments.