An improved edge detection algorithm for depth map inpainting
Three-dimensional (3D) measurement technology has been widely used in many scientific and engineering areas. The emergence of Kinect sensor makes 3D measurement much easier. However the depth map captured by Kinect sensor has some invalid regions, especially at object boundaries. These missing regio...
Saved in:
Published in | Optics and lasers in engineering Vol. 55; pp. 69 - 77 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.04.2014
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Three-dimensional (3D) measurement technology has been widely used in many scientific and engineering areas. The emergence of Kinect sensor makes 3D measurement much easier. However the depth map captured by Kinect sensor has some invalid regions, especially at object boundaries. These missing regions should be filled firstly. This paper proposes a depth-assisted edge detection algorithm and improves existing depth map inpainting algorithm using extracted edges. In the proposed algorithm, both color image and raw depth data are used to extract initial edges. Then the edges are optimized and are utilized to assist depth map inpainting. Comparative experiments demonstrate that the proposed edge detection algorithm can extract object boundaries and inhibit non-boundary edges caused by textures on object surfaces. The proposed depth inpainting algorithm can predict missing depth values successfully and has better performance than existing algorithm around object boundaries.
•Propose a depth-assisted edge detection algorithm using both color and depth data.•It can extract boundaries and eliminate non-boundary edges in the mean time.•Propose an improved depth map inpainting algorithm based on the extracted edges.•It is more accurate than existing algorithms, especially around object boundaries.•The algorithm can also be used for depth map obtained by stereo methods. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0143-8166 1873-0302 |
DOI: | 10.1016/j.optlaseng.2013.10.025 |