Learning Implicit Fields for Generative Shape Modeling

Zhiqin Chen; Hao Zhang

CVPR 2019poster1851 citations

Learning Implicit Fields for Generative Shape Modeling

Abstract

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.

BibTeX

@inproceedings{cvpr2019_learningimplicit,
  title = {Learning Implicit Fields for Generative Shape Modeling},
  author = {Zhiqin Chen and Hao Zhang},
  booktitle = {CVPR 2019},
  year = {2019}
}