Autors: Petkova, R. R., Bozhilov, I. B., Tonchev K., Manolova, A. H., Poulkov, V. K.
Title: Learning 3D Rotations from Point Cloud Data
Keywords: 3D rotation prediction, Graph convolutions, Point clouds

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.

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Issue

60th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2025 - Proceedings, 2025, Macedonia, https://doi.org/10.1109/ICEST66328.2025.11098312

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Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus