3D Surface Reconstruction by Self-Consistent Fusion of Shading and Shadow Features

Abstract

A novel framework for three-dimensional surface reconstruction by self-consistent fusion of shading and shadow features is presented. Based on the analysis of at least two pixel-synchronous images of the scene under different illumination conditions, this framework combines a shape from shading approach for estimating surface gradients and altitude variations with a shadow analysis that allows for an accurate determination of altitude differences on the surface. As a first step, the result of shadow analysis is used for selecting a consistent solution of the shape from shading reconstruction algorithm. As a second step, an additional error term derived from the fine structure of the shadow is incorporated into the reconstruction algorithm. This framework is applied to three-dimensional reconstruction of regions on the lunar surface using ground based CCD images. Beyond the planetary science scenario, it is applicable to classical machine vision tasks such as surface inspection in the context of industrial quality control.