Adversarial Generation of Continuous Implicit Shape Representations

Abstract

This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations. While the deep generation of shapes has been mostly tackled by voxel and surface point cloud approaches, our generator learns to approximate the signed distance for any point in space given prior latent information. Although structurally similar to generative point cloud approaches, this formulation can be evaluated with arbitrary point density during inference, leading to fine-grained details in generated outputs. Furthermore, we study the effects of using either progressively growing voxel- or point-processing networks as discriminators, and propose a refinement scheme to strengthen the generator's capabilities in modeling the zero iso-surface decision boundary of shapes. We train our approach on the ShapeNet benchmark dataset and validate, both quantitatively and qualitatively, its performance in generating realistic 3D shapes.

arXiv commentPublished in Eurographics 2020 - Short Papers

References in corpus (5)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• MeshCNN: A Network with an Edge
• Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
• SurfNet: Generating 3D shape surfaces using deep residual networks

Cited by in corpus (2)

• GIFS: Neural Implicit Function for General Shape Representation
• MARF: The Medial Atom Ray Field Object Representation