Junhua Xi1, Kouquan Zheng1, Yifan Zhong2, Longjiang Li3, Zhiping Cai1,*, Jinjing Chen4
Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3099-3111, 2023, DOI:10.32604/iasc.2023.030298
Abstract In geometry processing, symmetry research benefits from global geometric features of complete shapes, but the shape of an object captured in real-world applications is often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Different from the existing works predicting symmetry from the complete shape, we propose a learning approach for symmetry prediction based on a single RGB-D image. Instead of directly predicting the symmetry from incomplete shapes, our method consists of two modules, i.e., the multi-modal feature fusion module and the detection-by-reconstruction module. Firstly, we build a channel-transformer network (CTN) to extract cross-fusion features from the RGB-D… More >
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