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  • title: Learning 3D Dense Correspondence via Canonical Point Autoencoder
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            Learning 3D Dense Correspondence via Canonical Point Autoencoder
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            Learning 3D Dense Correspondence via Canonical Point Autoencoder

            Dec 6, 2021

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            AC

            An-Chieh Cheng

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            XL

            Xueting Li

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            MS

            Min Sun

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            About

            We propose a canonical point autoencoder (CPAE) that predicts dense correspondences between 3D shapes of the same category. The autoencoder performs two key functions: (a) encoding an arbitrarily ordered point cloud to a canonical primitive, e.g., a sphere, and (b) decoding the primitive back to the original input instance shape. As being placed in the bottleneck, this primitive plays a key role to map all the unordered point clouds on the canonical surface, and to be reconstructed in an ordered…

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