# Shape Completion Enabled Robotic Grasping

arXiv: https://arxiv.org/abs/1609.08546 · HTML: https://mlsanity.com/paper/1609.08546/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2016.

## Built on by (most cited first, 30 of 79)

- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation](https://mlsanity.com/paper/2203.01929/) (2022)
- [AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers](https://mlsanity.com/paper/2301.04545/) (2023)
- [PointGuard: Provably Robust 3D Point Cloud Classification](https://mlsanity.com/paper/2103.03046/) (2021)
- [Co-Occ: Coupling Explicit Feature Fusion with Volume Rendering Regularization for Multi-Modal 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2404.04561/) (2024)
- [Amodal Ground Truth and Completion in the Wild](https://mlsanity.com/paper/2312.17247/) (2023)
- [RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images](https://mlsanity.com/paper/2103.02184/) (2021)
- [Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification](https://mlsanity.com/paper/2111.10990/) (2021)
- [Cross-modal Learning for Image-Guided Point Cloud Shape Completion](https://mlsanity.com/paper/2209.09552/) (2022)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator](https://mlsanity.com/paper/2307.08492/) (2023)
- [Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition](https://mlsanity.com/paper/2303.15818/) (2023)
- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [DDGC: Generative Deep Dexterous Grasping in Clutter](https://mlsanity.com/paper/2103.04783/) (2021)
- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion](https://mlsanity.com/paper/2104.09587/) (2021)
- [Isometric 3D Adversarial Examples in the Physical World](https://mlsanity.com/paper/2210.15291/) (2022)
- [Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction](https://mlsanity.com/paper/2204.13998/) (2022)
- [Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter](https://mlsanity.com/paper/2311.05779/) (2023)
- [Composing Pick-and-Place Tasks By Grounding Language](https://mlsanity.com/paper/2102.08094/) (2021)
- [Denoise and Contrast for Category Agnostic Shape Completion](https://mlsanity.com/paper/2103.16671/) (2021)
- [ICGNet: A Unified Approach for Instance-Centric Grasping](https://mlsanity.com/paper/2401.09939/) (2024)
- [PCDreamer: Point Cloud Completion Through Multi-view Diffusion Priors](https://mlsanity.com/paper/2411.19036/) (2024)
- [DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction](https://mlsanity.com/paper/2409.19972/) (2024)
- [ViHOPE: Visuotactile In-Hand Object 6D Pose Estimation with Shape Completion](https://mlsanity.com/paper/2309.05662/) (2023)
- [3DSGrasp: 3D Shape-Completion for Robotic Grasp](https://mlsanity.com/paper/2301.00866/) (2023)
- [ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping](https://mlsanity.com/paper/2504.10857/) (2025)

## Abstract (arXiv)

This work provides an architecture to enable robotic grasp planning via shape completion. Shape completion is accomplished through the use of a 3D convolutional neural network (CNN). The network is trained on our own new open source dataset of over 440,000 3D exemplars captured from varying viewpoints. At runtime, a 2.5D pointcloud captured from a single point of view is fed into the CNN, which fills in the occluded regions of the scene, allowing grasps to be planned and executed on the completed object. Runtime shape completion is very rapid because most of the computational costs of shape completion are borne during offline training. We explore how the quality of completions vary based on several factors. These include whether or not the object being completed existed in the training data and how many object models were used to train the network. We also look at the ability of the network to generalize to novel objects allowing the system to complete previously unseen objects at runtime. Finally, experimentation is done both in simulation and on actual robotic hardware to explore the relationship between completion quality and the utility of the completed mesh model for grasping.