Authors:
Kazuki Matsumoto
;
Francois de Sorbier
and
Hideo Saito
Affiliation:
Keio University, Japan
Keyword(s):
Depth map, ToF depth sensor, GPU, Plane Fitting, Upsampling, denoising.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Health Engineering and Technology Applications
;
Image-Based Modeling
;
Pattern Recognition
;
Shape Representation
;
Signal Processing
;
Software Engineering
Abstract:
Recent advances of ToF depth sensor devices enables us to easily retrieve scene depth data with high frame
rates. However, the resolution of the depth map captured from these devices is much lower than that of color
images and the depth data suffers from the optical noise effects. In this paper, we propose an efficient algorithm
that upsamples depth map captured by ToF depth cameras and reduces noise. The upsampling is carried out
by applying plane based interpolation to the groups of points similar to planar structures and depth variance
based joint bilateral upsampling to curved or bumpy surface points. For dividing the depth map into piecewise
planar areas, we apply superpixel segmentation and graph component labeling. In order to distinguish planar
areas and curved areas, we evaluate the reliability of detected plane structures. Compared with other state-of-the-
art algorithms, our method is observed to produce an upsampled depth map that is smoothed and closer to
the ground trut
h depth map both visually and numerically. Since the algorithm is parallelizable, it can work in
real-time by utilizing highly parallel processing capabilities of modern commodity GPUs.
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