  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$^2$IM-Net,
encodes it into global and local features which are respectively fed into two
decoders. The base decoder uses the global features to reconstruct a coarse
implicit field, while the detail decoder reconstructs, from the local features,
two displacement maps, defined over the front and back sides of the captured
object. The final 3D reconstruction is a fusion between the base shape and the
displacement maps, with three losses enforcing the recovery of coarse shape,
overall structure, and surface details via a novel Laplacian term.
