Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Nonlocal Image and Movie Denoising
International Journal of Computer Vision
Fast acquisition of dense depth data by a new structured light scheme
Computer Vision and Image Understanding
Principal neighborhood dictionaries for nonlocal means image denoising
IEEE Transactions on Image Processing
A comprehensive framework for image inpainting
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Spatial and Temporal Enhancement of Depth Images Captured by a Time-of-Flight Depth Sensor
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Simultaneous structure and texture image inpainting
IEEE Transactions on Image Processing
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Guided depth enhancement via a fast marching method
Image and Vision Computing
Automatic objects segmentation with RGB-D cameras
Journal of Visual Communication and Image Representation
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Depth acquisition becomes inexpensive after the revolutionary invention of Kinect. For computer vision applications, depth maps captured by Kinect require additional processing to fill up missing parts. However, conventional inpainting methods for color images cannot be applied directly to depth maps as there are not enough cues to make accurate inference about scene structures. In this paper, we propose a novel fusion based inpainting method to improve depth maps. The proposed fusion strategy integrates conventional inpainting with the recently developed non-local filtering scheme. The good balance between depth and color information guarantees an accurate inpainting result. Experimental results show the mean absolute error of the proposed method is about 20mm, which is comparable to the precision of the Kinect sensor.