# Beyond Top-Grasps Through Scene Completion

arXiv: https://arxiv.org/abs/1909.12908 · HTML: https://mlsanity.com/paper/1909.12908/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [DDGC: Generative Deep Dexterous Grasping in Clutter](https://mlsanity.com/paper/2103.04783/) (2021)
- [You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects](https://mlsanity.com/paper/2404.03462/) (2024)
- [Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection](https://mlsanity.com/paper/2407.15771/) (2024)
- [VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction](https://mlsanity.com/paper/2203.04874/) (2022)
- [PCF-Grasp: Converting Point Completion to Geometry Feature to Enhance 6-DoF Grasp](https://mlsanity.com/paper/2504.16320/) (2025)
- [GP-net: Flexible Viewpoint Grasp Proposal](https://mlsanity.com/paper/2209.10404/) (2022)
- [Exploiting Radiance Fields for Grasp Generation on Novel Synthetic Views](https://mlsanity.com/paper/2505.11467/) (2025)
- [QAL: A Loss for Recall Precision Balance in 3D Reconstruction](https://mlsanity.com/paper/2511.17824/) (2025)

## Abstract (arXiv)

Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps. In this work, we present a method that allows end-to-end top-grasp planning methods to generate full six-degree-of-freedom grasps using a single RGB-D view as input. This is achieved by estimating the complete shape of the object to be grasped, then simulating different viewpoints of the object, passing the simulated viewpoints to an end-to-end grasp generation method, and finally executing the overall best grasp. The method was experimentally validated on a Franka Emika Panda by comparing 429 grasps generated by the state-of-the-art Fully Convolutional Grasp Quality CNN, both on simulated and real camera images. The results show statistically significant improvements in terms of grasp success rate when using simulated images over real camera images, especially when the real camera viewpoint is angled. Code and video are available at https://irobotics.aalto.fi/beyond-top-grasps-through-scene-completion/.