Stereo Matching Low-Textured Survey

1. Stereo Matching Low-Textured Survey Stereo Matching-Based Low-Textured Scene Reconstruction for Autonomous Land Vehicles (ALV) Navigation A Robust Stereo Matching Method for Low Texture Stereo Images

2. Outline • Introduction • Proposed Paper 1 • Proposed Paper 2 • Conclusion • Result

3. Introduction • Low-textured • Matching costs of the stereo pairs are almost similar. • In low-textured regions • Local algorithms are guaranteed to fail. • Global algorithms are too time-consuming.

4. Introduction • Solution of Local Approach • Bigger window size. • Low-textured regions are larger than the size of the aggregation window.

5. Introduction • The size of aggregation windows should be • large enough to include intensity variation. • small enough to avoid disparity variation. • An adaptive method for selecting the optimal aggregation window for stereo pairs.

6. Introduction • Low computation time and high quality of disparity map. • Different strategies are applied in the well-textured and low-textured regions.

7. Introduction

8. Outline • Introduction • Proposed Paper 1 • Proposed Method • Texture Detection • Approaches • Proposed Paper 2 • Conclusion • Result

9. Stereo Matching-Based Low-Textured Scene Reconstruction for Autonomous Land Vehicles (ALV) Navigation Mechatronics & Automation School, National University of Defense Technology, Changsha, Hunan, China Tingbo Hu Tao Wu HangenHe Image Analysis and Signal Processing (IASP), 2011 International Conference on

10. Proposed Method • Local algorithms are used to matching the pixels in well-textured regions. • A new matching algorithm combining plane priors and pixel dissimilarity is designed for the low-textured regions.

11. Proposed Method • In low-textured regions, the intensities of the pixels are almost identical. • Materialand the Normal Vectors are consistent.

12. Proposed Method • A low-textured region is likely to correspond to a 3D plane.

13. Texture Detection • . • .

14. Approach - Local • In the well-textured regions • Moravec Normalized Cross Correlation (MNCC)

15. Approach - Plane • In the low-textured regions

16. Approaches • . Low textured Well textured

17. Disparity Map

18. Outline • Introduction • Proposed Paper 1 • Proposed Paper 2 • Proposed Method • Edge Detection • Aggregation • Conclusion • Result

19. A Robust Stereo Matching Method for Low Texture Stereo Images Department of Information Media Technology Faculty of Information Science and Technology, Tokai University Le Thanh SACH Kiyoaki ATSUTA Kazuhiko HAMAMOTO Shozo KONDO Computing and Communication Technologies, 2009. RIVF '09. International Conference on

20. Proposed Method • Utilizes the edge maps computed from the stereo pairs to guide the cost aggregation.

21. Proposed Method

22. Edge Detection

23. Edge Map

24. Edge Detection

25. Aggregation • Horizontal Aggregation

26. Aggregation • Vertical Aggregation

27. Conclusion • Different strategies are applied in different kinds of regions. • The computational complexity of Paper 2cost aggregation method is independent of the window size.

28. Result