Paper Info
Title
Edge Preserving Range Image Smoothing by Rotated Bilateral Sampling
Abstract
In this paper we deal with (step and crease) edge preserving smoothing and normal estimation in low depth-resolution range images. As a result of short base distance stereo, compression or scene geometry, such range images consist of few discrete range values. We propose a method for smoothing such range images by interpolation while preserving boundary and crease edges, and estimating surface normals. The proposed method is based on rotated weighting sampling matrices and iso-range curves extracted from the range data. Samples and weights are selected using a rotated weight matrix and planar surfaces are fitted. Based on the fitting error, the best fit is selected to estimate the surface normal and interpolate surface points in such sparse dataset. Fitting error distribution is also used for estimating crease edge score. Such low-level understanding of range images can be utilized in further processing steps such as segmentation, localization, mapping, object detection etc. The output of the algorithm is presented and evaluated using both simulation and real range image data. It is shown that such sampling preserves crease edges and provides lower estimation errors for interpolated range image points.
Year
DOI
Venue
2015
10.1109/ECBS-EERC.2015.22
ECBS-EERC
Keywords
Field
DocType
sensors,noise,estimation,skeleton
Object detection,Computer vision,Computer science,Edge detection,Interpolation,Smoothing,Sampling (statistics),Artificial intelligence,Bilateral filter,Normal,Edge-preserving smoothing
Conference
Citations 
PageRank 
References 
0
0.34
10
Authors
3
Name
Order
Citations
PageRank
Viktor Kovács101.01
Kristof Csorba292.53
Gábor Tevesz373.17