Overview

2次元の画像から3次元の構造を推定する生成モデル.

Abstract

A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several datasets, including ShapeNet [2], and establish the first benchmarks in the literature. We also show how these models and their inference networks can be trained end-to-end from 2D images. This demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner.

Motivation

生成モデルの構築には今はやりのsequential generative modelを利用. 学習データはShapeNetを利用してます。

推定したモデルをOpenGLのレンダラで二次元に写像して、教師データ内の画像と比較なんて離れ業もやってます。面白い.

https://youtu.be/stvDAGQwL5c

Links

• arXiv