Robust Multi-View Stereo Without Matching

Abstract

This paper proposes a robust algorithm that finds a proxy surface from a series of calibrated pictures of an object without assuming any of its reflectance properties. This proxy is optimized to reduce view interpolation errors by globally minimizing the frequency criterion proposed in. The generality of this setting makes robustness particularly difficult to achieve since no model from which to identify outliers or noise is available. Unfortunately, failing to achieve robustness results in unusable proxy for most of the datasets presented. The traditional method of identifying outliers by their discrepency from photoconsistency must somehow be replaced by a global analysis involving all viewpoints. The major contribution of this paper is to meet this requirement by proposing a robust estimation of the minimizer of the frequency criterion as well as a novel framework for merging the multiple depth hypotheses obtained. View interpolation results and proxies are shown for challenging datasets, both Lambertian and not.