Modeling Images of Natural 3D Surfaces: Overview and Potential Applications

Abstract

Generative models of natural images have long been used in computer vision. However, since they only describe the statistics of 2D scenes, they fail to capture all the properties of the underlying 3D world. Even though such models are sufficient for many vision tasks, a 3D scene model is needed when it comes to inferring a 3D object or its characteristics. In this paper, we present such a generative model, incorporating both a multiscale surface prior model for surface geometry and reflectance, and an image formation process model based on realistic rendering, that accounts for the physics of image generation. We focus on the computation of the posterior model parameter densities, and on the critical aspects of the rendering. We also discuss how to efficiently invert the model within a Bayesian framework. We present a few potential applications, such as asteroid modeling and planetary topography recovery, illustrated by promising results on real images.