Learning Implicit Fields for Generative Shape Modeling

TL;DR

This paper introduces IM-NET, an implicit field decoder for 3D shape generation that enhances visual quality and outperforms traditional methods in shape modeling, interpolation, and reconstruction tasks.

Contribution

The paper presents IM-NET, a novel implicit field decoder that improves generative shape modeling and reconstruction quality over existing approaches.

Findings

IM-NET produces higher quality 3D shapes with better visual fidelity.

Replacing traditional decoders with IM-NET enhances shape interpolation.

IM-NET improves single-view 3D reconstruction results.

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.