# Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes

arXiv: https://arxiv.org/abs/2011.12001 · HTML: https://mlsanity.com/paper/2011.12001/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild](https://mlsanity.com/paper/2203.03089/) (2022)
- [Weakly-Supervised End-to-End CAD Retrieval to Scan Objects](https://mlsanity.com/paper/2203.12873/) (2022)
- [Recursive Cross-View: Use Only 2D Detectors to Achieve 3D Object Detection without 3D Annotations](https://mlsanity.com/paper/2211.07108/) (2022)

## Abstract (arXiv)

3D object detection has attracted much attention thanks to the advances in sensors and deep learning methods for point clouds. Current state-of-the-art methods like VoteNet regress direct offset towards object centers and box orientations with an additional Multi-Layer-Perceptron network. Both their offset and orientation predictions are not accurate due to the fundamental difficulty in rotation classification. In the work, we disentangle the direct offset into Local Canonical Coordinates (LCC), box scales and box orientations. Only LCC and box scales are regressed, while box orientations are generated by a canonical voting scheme. Finally, an LCC-aware back-projection checking algorithm iteratively cuts out bounding boxes from the generated vote maps, with the elimination of false positives. Our model achieves state-of-the-art performance on three standard real-world benchmarks: ScanNet, SceneNN and SUN RGB-D. Our code is available on https://github.com/qq456cvb/CanonicalVoting.