Abstract
3D shape pattern description of raw point clouds plays an essential and important role in 3D understanding. Previous works often learn feature representations via the solid cubic or spherical neighborhood, ignoring the distinction between the point distributions of objects in various shapes. Additionally, most works encode the spatial information in each neighborhood implicitly by learning edge weights between points, which is not enough to restore spatial information. In this paper, a Shape-Aware Feature Extraction (SAFE) module is proposed. It explicitly describes the spatial distribution of points in the neighborhood by well-designed distribution descriptors and replaces the conventional solid neighborhood with a hollow spherical neighborhood. Then, we encode the inner pattern and the outer pattern separately in the hollow spherical neighborhood to achieve shape awareness. Building an encoder-decoder network based on the SAFE module, we conduct extensive experiments and the results show that our SAFE-based network achieves state-of-the-art performance on the benchmark datasets ScanNet and ShapeNet.
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Acknowledgments
We thank for the support from National Natural Science Foundation of China (61972157, 61902129), Shanghai Pujiang Talent Program (19PJ1403100), Economy and Information Commission of Shanghai (XX-RGZN-01-19-6348), National Key Research and Development Program of China (No. 2019YFC1521104).
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Xu, J., Zhou, J., Tan, X., Ma, L. (2020). A Shape-Aware Feature Extraction Module for Semantic Segmentation of 3D Point Clouds. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_32
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