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We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined D^2IM-Net, encodes it into global and local features which arearXiv:2012.06650v2 fatcat:2aoj55etsbgd3bscz6aq7e2s3e