Transcript Stereo Matching-Based Low-Textured Scene Reconstruction

Stereo Matching Low-Textured Survey
1. Stereo Matching-Based Low-Textured Scene Reconstruction
for Autonomous Land Vehicles (ALV) Navigation
2. A Robust Stereo Matching Method for Low Texture Stereo
Images
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Outline
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Introduction
Proposed Paper 1
Proposed Paper 2
Conclusion
Result
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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.
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Introduction
• Solution of Local Approach
– Bigger window size.
• Low-textured regions are larger than the size
of the aggregation window.
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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.
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Introduction
• Low computation time and high quality of
disparity map.
• Different strategies are applied in the welltextured and low-textured regions.
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Introduction
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Outline
• Introduction
• Proposed Paper 1
– Proposed Method
– Texture Detection
– Approaches
• Proposed Paper 2
• Conclusion
• Result
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Stereo Matching-Based Low-Textured
Scene Reconstruction for Autonomous
Land Vehicles (ALV) Navigation
Image Analysis and Signal Processing (IASP), 2011 International
Conference on
Mechatronics & Automation School, National University of Defense
Technology, Changsha, Hunan, China
Tingbo Hu
Tao Wu
Hangen He
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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.
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Proposed Method
• In low-textured regions, the intensities of the
pixels are almost identical.
– Material and the Normal Vectors are consistent.
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Proposed Method
• A low-textured region is likely to correspond
to a 3D plane.
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Texture Detection
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Approach - Local
• In the well-textured regions
– Moravec Normalized Cross Correlation (MNCC)
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Approach - Plane
• In the low-textured regions
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Approaches
• .
Low textured
Well textured
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Disparity Map
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Outline
• Introduction
• Proposed Paper 1
• Proposed Paper 2
– Proposed Method
– Edge Detection
– Aggregation
• Conclusion
• Result
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A Robust Stereo Matching Method for Low
Texture Stereo Images
Computing and Communication Technologies, 2009. RIVF '09.
International Conference on
Department of Information Media Technology Faculty of Information
Science and Technology, Tokai University
Le Thanh SACH
Kiyoaki ATSUTA
Kazuhiko HAMAMOTO
Shozo KONDO
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Proposed Method
• Utilizes the edge maps computed from the
stereo pairs to guide the cost aggregation.
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Proposed Method
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Edge Detection
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Edge Map
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Edge Detection
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Aggregation
• Horizontal Aggregation
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Aggregation
• Vertical Aggregation
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Conclusion
• Different strategies are applied in different
kinds of regions.
• The computational complexity of Paper 2 cost
aggregation method is independent of the
window size.
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Result
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