# 3DSGrasp: 3D Shape-Completion for Robotic Grasp

arXiv: https://arxiv.org/abs/2301.00866 · HTML: https://mlsanity.com/paper/2301.00866/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 26 of 26)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generation](https://mlsanity.com/paper/1802.05384/) (2018)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [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)
- [Morphing and Sampling Network for Dense Point Cloud Completion](https://mlsanity.com/paper/1912.00280/) (2019)
- [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)
- [CAT: Cross Attention in Vision Transformer](https://mlsanity.com/paper/2106.05786/) (2021)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference](https://mlsanity.com/paper/1709.07599/) (2017)
- [Shape Completion Enabled Robotic Grasping](https://mlsanity.com/paper/1609.08546/) (2016)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [Learning Continuous 3D Reconstructions for Geometrically Aware Grasping](https://mlsanity.com/paper/1910.00983/) (2019)
- [Robust Grasp Planning Over Uncertain Shape Completions](https://mlsanity.com/paper/1903.00645/) (2019)
- [Online Object Model Reconstruction and Reuse for Lifelong Improvement of Robot Manipulation](https://mlsanity.com/paper/2109.13910/) (2021)
- [High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization](https://mlsanity.com/paper/1807.03407/) (2018)
- [Robotic Grasping through Combined Image-Based Grasp Proposal and 3D Reconstruction](https://mlsanity.com/paper/2003.01649/) (2020)
- [Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results](https://mlsanity.com/paper/2112.12053/) (2021)
- [TransSC: Transformer-based Shape Completion for Grasp Evaluation](https://mlsanity.com/paper/2107.00511/) (2021)

## Built on by (most cited first, 13 of 13)

- [ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping](https://mlsanity.com/paper/2504.10857/) (2025)
- [Progressive Text-to-3D Generation for Automatic 3D Prototyping](https://mlsanity.com/paper/2309.14600/) (2023)
- [Zero-Shot Multi-Object Scene Completion](https://mlsanity.com/paper/2403.14628/) (2024)
- [LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning](https://mlsanity.com/paper/2504.18424/) (2025)
- [High-Resolution Cranial Defect Reconstruction by Iterative, Low-Resolution, Point Cloud Completion Transformers](https://mlsanity.com/paper/2308.03813/) (2023)
- [Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering](https://mlsanity.com/paper/2306.07392/) (2023)
- [MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds](https://mlsanity.com/paper/2502.18745/) (2025)
- [SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting](https://mlsanity.com/paper/2507.23772/) (2025)
- [You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects](https://mlsanity.com/paper/2404.03462/) (2024)
- [GRASPLAT: Enabling dexterous grasping through novel view synthesis](https://mlsanity.com/paper/2510.19200/) (2025)
- [PCF-Grasp: Converting Point Completion to Geometry Feature to Enhance 6-DoF Grasp](https://mlsanity.com/paper/2504.16320/) (2025)
- [Grasp the Graph (GtG) 2.0: Ensemble of Graph Neural Networks for High-Precision Grasp Pose Detection in Clutter](https://mlsanity.com/paper/2505.02664/) (2025)
- [A Multi-Level Similarity Approach for Single-View Object Grasping: Matching, Planning, and Fine-Tuning](https://mlsanity.com/paper/2507.11938/) (2025)

## Abstract (arXiv)

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.