# Occlusion Resistant Object Rotation Regression from Point Cloud Segments

arXiv: https://arxiv.org/abs/1808.05498 · HTML: https://mlsanity.com/paper/1808.05498/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2018.

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

- [Projective Manifold Gradient Layer for Deep Rotation Regression](https://mlsanity.com/paper/2110.11657/) (2021)
- [CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds](https://mlsanity.com/paper/2103.01977/) (2021)
- [A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation](https://mlsanity.com/paper/2303.01743/) (2023)
- [Towards Unbiased Label Distribution Learning for Facial Pose Estimation Using Anisotropic Spherical Gaussian](https://mlsanity.com/paper/2208.09122/) (2022)
- [Towards Robust Probabilistic Modeling on SO(3) via Rotation Laplace Distribution](https://mlsanity.com/paper/2305.10465/) (2023)
- [3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction](https://mlsanity.com/paper/2411.00543/) (2024)
- [Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose](https://mlsanity.com/paper/2305.10808/) (2023)
- [Efficient 2D to Full 3D Human Pose Uplifting including Joint Rotations](https://mlsanity.com/paper/2504.09953/) (2025)
- [Jenga Stacking Based on 6D Pose Estimation for Architectural Form Finding Process](https://mlsanity.com/paper/2311.10918/) (2023)

## Abstract (arXiv)

Rotation estimation of known rigid objects is important for robotic applications such as dexterous manipulation. Most existing methods for rotation estimation use intermediate representations such as templates, global or local feature descriptors, or object coordinates, which require multiple steps in order to infer the object pose. We propose to directly regress a pose vector from raw point cloud segments using a convolutional neural network. Experimental results show that our method can potentially achieve competitive performance compared to a state-of-the-art method, while also showing more robustness against occlusion. Our method does not require any post processing such as refinement with the iterative closest point algorithm.