A Disparity Computation Framework

A disparity map is a key component of stereo vision systems. Autonomous navigation, 3D reconstruction and mobility are examples of areas that use disparity maps as an important element. Although much work has been done in the stereo vision field, it is not easy to build stereo systems with concepts such as reuse and extensible scope. In the present paper, we contribute to reducing this gap by presenting a software architecture that can accommodate different stereo methods through a new standard structure. Firstly, we introduce scenarios that illustrate use cases of disparity maps, and we show a novel architecture that foments code reuse. A Disparity Computation Framework (DCF) is presented and how its components are structured regarding compartmentalization are discussed. Then, we introduce a prototype that closely follows our proposal, and we describe some test cases that were performed. We conclude that the DCF can satisfy different on-demand scenarios and that it can support new stereo methods, functions, and evaluations for different applications without much effort.

[1]  G.T. Laureano,et al.  Disparities Maps Generation Employing Multi-resolution Analysis and Perceptual Grouping , 2008, 2008 First Workshops on Image Processing Theory, Tools and Applications.

[2]  Peter J. Clarke,et al.  A Communication Virtual Machine , 2006, 30th Annual International Computer Software and Applications Conference (COMPSAC'06).

[3]  Margrit Gelautz,et al.  Secrets of adaptive support weight techniques for local stereo matching , 2013, Comput. Vis. Image Underst..

[4]  Carsten Rother,et al.  Fast cost-volume filtering for visual correspondence and beyond , 2011, CVPR 2011.

[5]  Philippe Bekaert,et al.  Local Stereo Matching with Segmentation-based Outlier Rejection , 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06).

[6]  Stefano Mattoccia,et al.  A locally global approach to stereo correspondence , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[7]  Kamaljit Kaur,et al.  A Survey on Stereo Matching Techniques for 3D Vision in Image Processing , 2016 .

[8]  Gustavo Teodoro Laureano,et al.  Stereo Vision Methods: From Development to the Evaluation of Disparity Maps , 2017, 2017 Workshop of Computer Vision (WVC).

[9]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Aaron F. Bobick,et al.  Large Occlusion Stereo , 1999, International Journal of Computer Vision.

[11]  Andreas Geiger,et al.  Object scene flow for autonomous vehicles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Vijay Kumar,et al.  The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception , 2018, IEEE Robotics and Automation Letters.

[13]  A. Verri,et al.  A compact algorithm for rectification of stereo pairs , 2000 .

[14]  Ingemar J. Cox,et al.  A Maximum Likelihood Stereo Algorithm , 1996, Comput. Vis. Image Underst..

[15]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Ivan Laptev,et al.  Pose Estimation and Segmentation of People in 3D Movies , 2013, 2013 IEEE International Conference on Computer Vision.

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

[18]  Heiko Hirschmüller,et al.  Evaluation of Cost Functions for Stereo Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Olga Veksler,et al.  Fast variable window for stereo correspondence using integral images , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[20]  Kuk-Jin Yoon,et al.  Locally adaptive support-weight approach for visual correspondence search , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[21]  Manuela Chessa,et al.  Descriptor : A dataset of stereoscopic images and ground-truth disparity mimicking human fi xations in peripersonal space , 2017 .

[22]  Johannes L. Schönberger,et al.  Supplementary Material for A MultiView Stereo Benchmark with High-Resolution Images and Multi-Camera Videos , 2017 .