# PointVoteNet: Accurate Object Detection and 6 DoF Pose Estimation in Point Clouds

arXiv: https://arxiv.org/abs/1912.09057 · HTML: https://mlsanity.com/paper/1912.09057/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects](https://mlsanity.com/paper/2302.13075/) (2023)
- [BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects](https://mlsanity.com/paper/2403.09799/) (2024)
- [CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds](https://mlsanity.com/paper/2103.01977/) (2021)
- [Keypoint Cascade Voting for Point Cloud Based 6DoF Pose Estimation](https://mlsanity.com/paper/2210.08123/) (2022)
- [ParaPose: Parameter and Domain Randomization Optimization for Pose Estimation using Synthetic Data](https://mlsanity.com/paper/2203.00945/) (2022)
- [A survey of top-down approaches for human pose estimation](https://mlsanity.com/paper/2202.02656/) (2022)
- [Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness](https://mlsanity.com/paper/2207.09412/) (2022)

## Abstract (arXiv)

We present a learning-based method for 6 DoF pose estimation of rigid objects in point cloud data. Many recent learning-based approaches use primarily RGB information for detecting objects, in some cases with an added refinement step using depth data. Our method consumes unordered point sets with/without RGB information, from initial detection to the final transformation estimation stage. This allows us to achieve accurate pose estimates, in some cases surpassing state of the art methods trained on the same data.