Abstract：Some networks for processing 3D data lack rotation equivariance and have difficulty in processing 3D objects after unknown rotation and estimating their pose changes. To solve this problem, a three-dimensional rotation equivariant self-supervised learning vector network combined with diffusion model is proposed in this paper. The network is designed to learn the rotation information of 3D objects, handle the pose change estimation task, and optimize the overall pose information using the local pose information denoised by the diffusion model. For the equivariant vector network, the scalar data are promoted to vector representations using vector neurons. Self-supervised learning is implemented without labeled data to enable the network to learn the vector information of 3D targets and achieve rotation reconstruction and pose change estimation of 3D data. Meanwhile, to solve the problem of local deviation in the pose estimation results, a diffusion model is constructed to optimize the overall pose change estimation results. The model learns local pose information in the process of noising and denoising, and can effectively remove the noise in the local pose. Experiments demonstrate that the designed network can estimate pose changes of the data in 3D space when the test data are randomly rotated, and it outperforms other networks. Moreover, the proposed model achieves superior performance in the reassembly task compared with current state-of-the-art methods, and optimizes the overall pose information through local pose information.
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2025, Vol. 38 
