Abstract
In recent years, 3D point cloud semantic segmentation has made remarkable progress. However, most existing work focuses on designing intricate structures to aggregate local features, resulting in a significant number of parameters and computational demands. In this Paper, we combine the idea of structural reparameterization in 2D convolution to propose RPNet, which can effectively reduce the number of parameters while fully extracting the point cloud features. Specifically. We first design the multi-branch structure SRLFA (Structure Re-parameterization Local Feature Abstract) module based on the reparameterization to fully extract the local features of the point cloud, and design the PFA (Point Feature Abstract) module to extract the features of the point itself. Then, by decoupling the training and inference phases, the multi-branch structure is fused into an equivalent single-branch structure through the idea of structural reparameterization during training and inference, which ensures the feature extraction capability while effectively reducing the number of parameters. Finally, the proposed method is trained and tested on several public data, and the results demonstrate that the proposed method achieves advanced performance in mIoU and OA with effective control of the number of model parameters.
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Acknowledgement
This research was funded by the Basic Research Program of Qinghai Province (Grant No. 2021-ZJ-704) and Beijing Natural Science Foundation (GrantNo. 4212001).
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Li, Z., Jia, K., Zhao, Y., Huang, W. (2023). Structural Reparameterization Network on Point Cloud Semantic Segmentation. In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14355. Springer, Cham. https://doi.org/10.1007/978-3-031-46305-1_28
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DOI: https://doi.org/10.1007/978-3-031-46305-1_28
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