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.
Cite
Text
Chen and Zhang. "Learning Implicit Fields for Generative Shape Modeling." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00609Markdown
[Chen and Zhang. "Learning Implicit Fields for Generative Shape Modeling." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/chen2019cvpr-learning-b/) doi:10.1109/CVPR.2019.00609BibTeX
@inproceedings{chen2019cvpr-learning-b,
title = {{Learning Implicit Fields for Generative Shape Modeling}},
author = {Chen, Zhiqin and Zhang, Hao},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2019},
doi = {10.1109/CVPR.2019.00609},
url = {https://mlanthology.org/cvpr/2019/chen2019cvpr-learning-b/}
}