Stereo matching algorithm for 3D surface reconstruction based on triangulation principle

This article presents a new stereo matching algorithm based on local method. The absolute difference (AD) algorithm works fast and precise at low texture area, however this algorithm is sensitive to radiometric distortions (i.e., contrast or brightness) and low texture areas. To overcome these problems, the proposed algorithm utilizes a combination of AD, gradient difference (GM) and census transform (CT). The GM algorithm is robust against the radiometric errors and CT algorithm works well on the low texture regions. The proposed algorithm performs much better based on the experimental results of the Middlebury dataset. The disparity map from the result consists of depth information which requires for the three-dimensional (3D) surface reconstruction.

[1]  Shin'ichi Satoh Simple low-dimensional features approximating NCC-based image matching , 2011, Pattern Recognit. Lett..

[2]  Haidi Ibrahim,et al.  Literature Survey on Stereo Vision Disparity Map Algorithms , 2016, J. Sensors.

[3]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[4]  Julian Eggert,et al.  Anisotropic Median Filtering for Stereo Disparity Map Refinement , 2018, VISAPP.

[5]  Dah-Jye Lee,et al.  Dense Disparity Real-Time Stereo Vision Algorithm for Resource-Limited Systems , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Lifeng Sun,et al.  Virtual support window for adaptive-weight stereo matching , 2011, 2011 Visual Communications and Image Processing (VCIP).

[7]  Johannes Stallkamp,et al.  Real-time stereo vision: Optimizing Semi-Global Matching , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[8]  Eric Psota,et al.  Real-Time Stereo Matching on CUDA Using an Iterative Refinement Method for Adaptive Support-Weight Correspondences , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Enhua Wu,et al.  Constant Time Weighted Median Filtering for Stereo Matching and Beyond , 2013, 2013 IEEE International Conference on Computer Vision.

[10]  Andreas Geiger,et al.  Efficient Large-Scale Stereo Matching , 2010, ACCV.

[11]  Lifeng Sun,et al.  Binary stereo matching , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[12]  Yann LeCun,et al.  Computing the stereo matching cost with a convolutional neural network , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Dongxiao Li,et al.  Fast stereo matching using adaptive guided filtering , 2014, Image Vis. Comput..

[14]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Guijin Wang,et al.  High-Accuracy Stereo Matching Based on Adaptive Ground Control Points , 2015, IEEE Transactions on Image Processing.

[16]  Joost van de Weijer,et al.  Accurate Stereo Matching by Two-Step Energy Minimization , 2015, IEEE Transactions on Image Processing.

[17]  Rostam Affendi Hamzah,et al.  Stereo Matching Algorithm Based On Illumination Control To Improve The Accuracy , 2016 .

[18]  Jonathan M. Garibaldi,et al.  Real-Time Correlation-Based Stereo Vision with Reduced Border Errors , 2002, International Journal of Computer Vision.

[19]  Zhigeng Pan,et al.  Real-time stereo matching based on fast belief propagation , 2011, Machine Vision and Applications.

[20]  Truong Q. Nguyen,et al.  Local Disparity Estimation With Three-Moded Cross Census and Advanced Support Weight , 2013, IEEE Transactions on Multimedia.

[21]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[22]  Antonios Gasteratos,et al.  A stereo matching approach based on particle filters and scattered control landmarks , 2015, Image Vis. Comput..

[23]  Lei Zhang,et al.  Effective stereo matching using reliable points based graph cut , 2013, 2013 Visual Communications and Image Processing (VCIP).

[24]  Pau-Choo Chung,et al.  Efficient Disparity Estimation Using Hierarchical Bilateral Disparity Structure Based Graph Cut Algorithm With a Foreground Boundary Refinement Mechanism , 2013, IEEE Transactions on Circuits and Systems for Video Technology.