Near Real-Time Stereo With Adaptive Support Weight Approaches

Algorithms based on the adaptive support weight strategy currently represent the state-of-the-art in local ster eo matching. Unfortunately, their good-quality results come at the price of high computation times: As opposed to standard local algorithms, incremental computation via slidin g windows is not applicable for adaptive support weight windows. This paper presents a method for considerably speeding up computation times of these methods. The key idea is to exploit the adaptive support weight windows for generating an explicit over-segmentation of the reference image in a fast way. Having this explicit segmentation, we can take advantage of a modified “segmentation-based” sliding window technique, which makes run time independent of the window size. In particular, we demonstrate our transformation scheme for the geodesic stereo matcher of [11] that has recently produced excellent results. Our unoptimized GPU-based implementation processes 320 × 240 pixel images with 26 allowed disparities at 10 frames per second and achieves rank 32 out of 74 methods in the Middlebury online benchmark.

[1]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[2]  Laurent Moll,et al.  Real time correlation-based stereo: algorithm, implementations and applications , 1993 .

[3]  Takeo Kanade,et al.  A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Emanuele Trucco,et al.  Efficient stereo with multiple windowing , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Hai Tao,et al.  Global matching criterion and color segmentation based stereo , 2000, Proceedings Fifth IEEE Workshop on Applications of Computer Vision.

[6]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[7]  Peter Meer,et al.  Synergism in low level vision , 2002, Object recognition supported by user interaction for service robots.

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

[9]  Richard Szeliski,et al.  High-quality video view interpolation using a layered representation , 2004, SIGGRAPH 2004.

[10]  Li Hong,et al.  Segment-based stereo matching using graph cuts , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[11]  Reinhard Männer,et al.  Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation , 2004, International Journal of Computer Vision.

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

[13]  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).

[14]  Margrit Gelautz,et al.  A layered stereo matching algorithm using image segmentation and global visibility constraints , 2005 .

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

[16]  Ruigang Yang,et al.  A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching , 2007, International Journal of Computer Vision.

[17]  Federico Tombari,et al.  Segmentation-Based Adaptive Support for Accurate Stereo Correspondence , 2007, PSIVT.

[18]  Kwanghoon Sohn,et al.  Cost Aggregation and Occlusion Handling With WLS in Stereo Matching , 2008, IEEE Transactions on Image Processing.

[19]  Federico Tombari,et al.  Classification and evaluation of cost aggregation methods for stereo correspondence , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Margrit Gelautz,et al.  Local stereo matching using geodesic support weights , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).