A fusion framework of stereo vision and kinect for high-quality dense depth maps

Authors:
Yucheng Wang;Yunde Jia
Affiliations:
Beijing Lab of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, P.R. China;Beijing Lab of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, P.R. China
Venue:
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Year:
2012

Citing 10
Cited 0

A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond

Multiple view geometry in computer visiond
Fast Approximate Energy Minimization via Graph Cuts

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Miniature Stereo Vision Machine (MSVM-III) for Dense Disparity Mapping

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Joint bilateral upsampling

ACM SIGGRAPH 2007 papers
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors

IEEE Transactions on Pattern Analysis and Machine Intelligence
High-quality single-shot capture of facial geometry

ACM SIGGRAPH 2010 papers
Reliability Fusion of Time-of-Flight Depth and Stereo Geometry for High Quality Depth Maps

IEEE Transactions on Pattern Analysis and Machine Intelligence
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera

Proceedings of the 24th annual ACM symposium on User interface software and technology
An FPGA-based RGBD imager

Machine Vision and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a fusion framework of stereo vision and Kinect for high-quality dense depth maps. The fusion problem is formulated as maximum a posteriori estimation of the Markov random field using the Bayes rule. We design a global energy function with a novel data term, which provides a reasonable, straight-forward and scalable way to fuse stereo vision and the depth data from Kinect. Particularly, visibility and pixelwise noises of the depth data from Kinect are taken into account in our fusion approach. Experimental results demonstrate effectiveness and accuracy of the proposed framework.