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• W2798490118 abstract "The availability of affordable 3D full body reconstruction systems has given rise to free-viewpoint video (FVV) of human shapes. Most existing solutions produce temporally uncorrelated point clouds or meshes with unknown point/vertex correspondences. Individually compressing each frame is ineffective and still yields to ultra-large data sizes. We present an end-to-end deep learning scheme to establish dense shape correspondences and subsequently compress the data. Our approach uses sparse set of panoramic depth maps or PDMs, each emulating an inward-viewing concentric mosaics (CM) [45]. We then develop a learning-based technique to learn pixel-wise feature descriptors on PDMs. The results are fed into an autoencoder-based network for compression. Comprehensive experiments demonstrate our solution is robust and effective on both public and our newly captured datasets." @default.
• W2798490118 created "2018-05-07" @default.
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• W2798490118 date "2018-06-01" @default.
• W2798490118 modified "2023-09-22" @default.
• W2798490118 title "4D Human Body Correspondences from Panoramic Depth Maps" @default.
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• W2798490118 doi "https://doi.org/10.1109/cvpr.2018.00304" @default.
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