# Learning Continuous 3D Reconstructions for Geometrically Aware Grasping

arXiv: https://arxiv.org/abs/1910.00983 · HTML: https://mlsanity.com/paper/1910.00983/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 29 of 29)

- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [Learning Multi-Object Dynamics with Compositional Neural Radiance Fields](https://mlsanity.com/paper/2202.11855/) (2022)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning](https://mlsanity.com/paper/2110.00792/) (2021)
- [Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction](https://mlsanity.com/paper/2204.13998/) (2022)
- [ICGNet: A Unified Approach for Instance-Centric Grasping](https://mlsanity.com/paper/2401.09939/) (2024)
- [3DSGrasp: 3D Shape-Completion for Robotic Grasp](https://mlsanity.com/paper/2301.00866/) (2023)
- [Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds](https://mlsanity.com/paper/2110.04685/) (2021)
- [Where To Start? Transferring Simple Skills to Complex Environments](https://mlsanity.com/paper/2212.06111/) (2022)
- [ScrewSplat: An End-to-End Method for Articulated Object Recognition](https://mlsanity.com/paper/2508.02146/) (2025)
- [Perceiving Unseen 3D Objects by Poking the Objects](https://mlsanity.com/paper/2302.13375/) (2023)
- [Shape Completion with Prediction of Uncertain Regions](https://mlsanity.com/paper/2308.00377/) (2023)
- [DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects](https://mlsanity.com/paper/2305.04449/) (2023)
- [Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand](https://mlsanity.com/paper/2310.20350/) (2023)
- [Anthropomorphic Grasping with Neural Object Shape Completion](https://mlsanity.com/paper/2311.02510/) (2023)
- [Simulation-based Bayesian inference for robotic grasping](https://mlsanity.com/paper/2303.05873/) (2023)
- [Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction](https://mlsanity.com/paper/2305.09510/) (2023)
- [DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation](https://mlsanity.com/paper/2309.14463/) (2023)
- [Towards Confidence-guided Shape Completion for Robotic Applications](https://mlsanity.com/paper/2209.04300/) (2022)
- [Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch](https://mlsanity.com/paper/2212.08604/) (2022)
- [Learning Neural Radiance Fields from Multi-View Geometry](https://mlsanity.com/paper/2210.13041/) (2022)
- [Informative Rays Selection for Few-Shot Neural Radiance Fields](https://mlsanity.com/paper/2312.17561/) (2023)
- [Improving Soft-Capture Phase Success in Space Debris Removal Missions: Leveraging Deep Reinforcement Learning and Tactile Feedback](https://mlsanity.com/paper/2409.12273/) (2024)
- [A Review of Scene Representations for Robot Manipulators](https://mlsanity.com/paper/2301.11275/) (2022)
- [Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping](https://mlsanity.com/paper/2304.08805/) (2023)
- [Leveraging Foundation Models To learn the shape of semi-fluid deformable objects](https://mlsanity.com/paper/2411.16802/) (2024)
- [Robust Bayesian Scene Reconstruction with Retrieval-Augmented Priors for Precise Grasping and Planning](https://mlsanity.com/paper/2411.19461/) (2024)
- [LensDFF: Language-enhanced Sparse Feature Distillation for Efficient Few-Shot Dexterous Manipulation](https://mlsanity.com/paper/2503.03890/) (2025)
- [DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation](https://mlsanity.com/paper/2506.18779/) (2025)

## Abstract (arXiv)

Deep learning has enabled remarkable improvements in grasp synthesis for previously unseen objects from partial object views. However, existing approaches lack the ability to explicitly reason about the full 3D geometry of the object when selecting a grasp, relying on indirect geometric reasoning derived when learning grasp success networks. This abandons explicit geometric reasoning, such as avoiding undesired robot object collisions. We propose to utilize a novel, learned 3D reconstruction to enable geometric awareness in a grasping system. We leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization. We additionally explicitly constrain the optimization to avoid undesired contact, directly using the reconstruction. We examine the role of geometry in grasping both in the training of grasp metrics and through 96 robot grasping trials. Our results can be found on https://sites.google.com/view/reconstruction-grasp/.