Outlier removal and discontinuity preserving smoothing of range data

Abstract

Algorithms for the segmentation and description of range images are very sensitive to errors in the source data caused by noise processes in the optoelectronic sensing, and outliers caused by incorrect signal detection, for example false peaks in an active laser triangulation system. The authors present an approach to range data processing designed to reconstruct the underlying shape of the surfaces in the scene, yet preserve the discontinuities between them. The approach has two stages, first outlier removal by a lower complexity variation of the least median of squares estimator, and second, robust smoothing by anisotropic diffusion. To evaluate the proposed methods, the authors quantify the improvement in depth, normal and curvature estimation, and show how preprocessing improves surface patch segmentation and classification.