Learning 3D Rotations from Point Cloud DataTitle
Abstract
This paper presents a deep learning (DL) based method for 3D rotation prediction on point cloud data. The proposed approach utilizes a single graph convolutional layer to capture meaningful geometric features and further regresses rotational quaternion via shallow multi-layer perceptron (MLP). Experimental results show that the method outperforms traditional registration algorithms, such as Iterative Closest Point (ICP), across multiple evaluation metrics, and also surpasses several DL-based approaches in various criteria. Moreover, the model is designed with only a few thousand parameters, making it highly lightweight and computationally efficient.
Authors
- Radostina Petkova
- Ivaylo Bozhilov
- Krasimir Tonchev
- Agata Manolova
- Vladimir Poulkov
Venue
2025 60th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST)
Links
https://ieeexplore.ieee.org/document/11098312