Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image

TL;DR

This paper introduces Probabilistic Reconstruction Networks, a principled probabilistic framework for 3D shape inference from a single image, achieving state-of-the-art results on ShapeNet with a simple voxel grid representation.

Contribution

It presents a novel probabilistic inference-based framework that decouples shape representation from inference and supports different training regimes.

Findings

Sets new state-of-the-art on ShapeNet for 3D reconstruction

Achieves superior results with simple voxel grid representation

Supports Monte Carlo and variational training approaches

Abstract

We study end-to-end learning strategies for 3D shape inference from images, in particular from a single image. Several approaches in this direction have been investigated that explore different shape representations and suitable learning architectures. We focus instead on the underlying probabilistic mechanisms involved and contribute a more principled probabilistic inference-based reconstruction framework, which we coin Probabilistic Reconstruction Networks. This framework expresses image conditioned 3D shape inference through a family of latent variable models, and naturally decouples the choice of shape representations from the inference itself. Moreover, it suggests different options for the image conditioning and allows training in two regimes, using either Monte Carlo or variational approximation of the marginal likelihood. Using our Probabilistic Reconstruction Networks we obtain single image 3D reconstruction results that set a new state of the art on the ShapeNet dataset in terms of the intersection over union and earth mover's distance evaluation metrics. Interestingly, we obtain these results using a basic voxel grid representation, improving over recent work based on finer point cloud or mesh based representations.