# Robotic Grasping through Combined Image-Based Grasp Proposal and 3D Reconstruction

arXiv: https://arxiv.org/abs/2003.01649 · HTML: https://mlsanity.com/paper/2003.01649/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization](https://mlsanity.com/paper/2306.16928/) (2023)
- [3DSGrasp: 3D Shape-Completion for Robotic Grasp](https://mlsanity.com/paper/2301.00866/) (2023)
- [TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance](https://mlsanity.com/paper/2207.07861/) (2022)
- [Uncertainty-driven Exploration Strategies for Online Grasp Learning](https://mlsanity.com/paper/2309.12038/) (2023)
- [Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking](https://mlsanity.com/paper/2307.16488/) (2023)
- [Towards Confidence-guided Shape Completion for Robotic Applications](https://mlsanity.com/paper/2209.04300/) (2022)
- [Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive Consistency Constraints](https://mlsanity.com/paper/2309.09739/) (2023)
- [Human Multi-View Synthesis from a Single-View Model:Transferred Body and Face Representations](https://mlsanity.com/paper/2412.03011/) (2024)
- [Object Pose and Shape Estimation for Grasping: Does it Work?](https://mlsanity.com/paper/2605.26944/) (2026)

## Abstract (arXiv)

We present a novel approach to robotic grasp planning using both a learned grasp proposal network and a learned 3D shape reconstruction network. Our system generates 6-DOF grasps from a single RGB-D image of the target object, which is provided as input to both networks. By using the geometric reconstruction to refine the the candidate grasp produced by the grasp proposal network, our system is able to accurately grasp both known and unknown objects, even when the grasp location on the object is not visible in the input image. This paper presents the network architectures, training procedures, and grasp refinement method that comprise our system. Experiments demonstrate the efficacy of our system at grasping both known and unknown objects (91% success rate in a physical robot environment, 84% success rate in a simulated environment). We additionally perform ablation studies that show the benefits of combining a learned grasp proposal with geometric reconstruction for grasping, and also show that our system outperforms several baselines in a grasping task.