Edge-aware normal estimation by rotated bilateral sampling

Abstract

In this paper we deal with edge preserving surface normal estimation and crease edge detection in discretized range images. Such range images consist of few discrete quantization levels due to the data acquisition method (short base distance stereo), or when the distance variation of the examined surface is low, compared to the disparity quantization levels. We propose a method for normal estimation and crease edge detection using iso-range curves and rotated bilateral filter based sampling. Iso-range curves are used to extract sparse, but reliable range image points. Samples are first selected by a rotated weight matrix and a plane is fitted on such samples. Simple statistics are gathered during the rotation of the weight matrix, in order to find the best fitting plane and extract crease edge measure. Such information may be used for further range image processing: segmentation, mapping, localization, object detection, recognition etc. Results are shown for both synthetic and real range images. It was shown that applying the proposed method resulted in more accurate normal estimations, crease edges were not smoothed and crease edges were successfully detected.