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Real-time stereo matching using orthogonal reliability-based dynamic programming.

Minglun Gong, Yee-Hong Yang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 16, 2007
    PubMed
    Summary
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    This study introduces a novel real-time stereo matching algorithm, generating semi-dense disparity maps efficiently. The method utilizes programmable graphics hardware for high-speed, parallel processing, achieving reliable matches at 10-20 frames per second.

    Area of Science:

    • Computer Vision
    • Real-time Image Processing
    • Stereo Vision

    Background:

    • Traditional stereo matching algorithms often struggle with real-time performance and computational demands.
    • Dynamic programming offers a robust framework but can be computationally intensive.
    • The need for efficient, high-speed stereo matching is critical in applications like robotics and autonomous systems.

    Purpose of the Study:

    • To develop a novel, real-time algorithm for estimating reliable stereo matches.
    • To improve the efficiency and speed of disparity map generation using dynamic programming principles.
    • To leverage programmable graphics hardware for accelerated computation.

    Main Methods:

    • A novel algorithm based on dynamic programming for semi-dense disparity map generation.

    Related Experiment Videos

  • Reduced dynamic programming passes to two, replacing iterative best path tracing with local minimum searching.
  • Implementation of most computations on programmable graphics hardware (GPU).

    • Main Results:

    • The algorithm achieves real-time performance, processing at 10-20 frames per second on an ATI Radeon X800.
    • Reliable stereo matches are generated for approximately 60%-80% of pixels.
    • The algorithm can be configured to produce full-density disparity maps if required.

    Conclusions:

    • The proposed algorithm offers a significant advancement in real-time stereo matching.
    • The use of GPU acceleration and optimized dynamic programming enables high-speed, reliable disparity estimation.
    • This method is suitable for real-time computer vision applications requiring accurate depth perception.