  We advocate the use of implicit fields for learning generative models of
shapes and introduce an implicit field decoder, called IM-NET, for shape
generation, aimed at improving the visual quality of the generated shapes. An
implicit field assigns a value to each point in 3D space, so that a shape can
be extracted as an iso-surface. IM-NET is trained to perform this assignment by
means of a binary classifier. Specifically, it takes a point coordinate, along
with a feature vector encoding a shape, and outputs a value which indicates
whether the point is outside the shape or not. By replacing conventional
decoders by our implicit decoder for representation learning (via IM-AE) and
shape generation (via IM-GAN), we demonstrate superior results for tasks such
as generative shape modeling, interpolation, and single-view 3D reconstruction,
particularly in terms of visual quality. Code and supplementary material are
available at https://github.com/czq142857/implicit-decoder.
