3D Shape Induction from 2D Views of Multiple Objects

Abstract

In this paper we investigate the problem of inducing a distribution over three-dimensional structures given two-dimensional views of multiple objects taken from unknown viewpoints. Our approach called "projective generative adversarial networks" (PrGANs) trains a deep generative model of 3D shapes whose projections match the distributions of the input 2D views. The addition of a projection module allows us to infer the underlying 3D shape distribution without using any 3D, viewpoint information, or annotation during the learning phase. We show that our approach produces 3D shapes of comparable quality to GANs trained on 3D data for a number of shape categories including chairs, airplanes, and cars. Experiments also show that the disentangled representation of 2D shapes into geometry and viewpoint leads to a good generative model of 2D shapes. The key advantage is that our model allows us to predict 3D, viewpoint, and generate novel views from an input image in a completely unsupervised manner.

Submitted to CVPR 2017

References in corpus (6)

• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• Deep Convolutional Inverse Graphics Network
• A Kernel Method for the Two-Sample Problem
• Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
• 3D ShapeNets: A Deep Representation for Volumetric Shapes

Cited by in corpus (3)

• Rethinking Reprojection: Closing the Loop for Pose-aware ShapeReconstruction from a Single Image
• 3D Object Reconstruction from a Single Depth View with Adversarial Learning
• End-to-end 3D shape inverse rendering of different classes of objects from a single input image