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Rate-constrained 3D surface estimation from noise-corrupted multiview depth videos.

Wenxiu Sun, Gene Cheung, Philip A Chou

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 31, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study jointly denoises and compresses noisy depth maps for 3D scenes, improving virtual view synthesis. A novel rate-constrained method optimizes 3D surface reconstruction, reducing coding rates by up to 32%.

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    Area of Science:

    • Computer Vision
    • Signal Processing
    • 3D Graphics

    Background:

    • Depth maps from 3D sensors are crucial for virtual imaging but suffer from acquisition noise.
    • Denoising and compression are essential for efficient transmission of depth data.
    • Separate denoising and compression stages may not yield optimal results.

    Purpose of the Study:

    • To jointly address depth map denoising and compression for dynamic 3D scenes.
    • To develop a rate-constrained estimation framework for improved 3D surface reconstruction.
    • To enhance the quality of synthesized virtual views at the decoder.

    Main Methods:

    • Formulation of a rate-constrained maximum a posteriori (MAP) estimation problem.
    • Iterative algorithm optimizing 3D surface structure and texture alternately.
    • Utilizing Multi-View Coding (MVC) for compression of multiview depth video.

    Main Results:

    • The proposed rate-constrained MAP solution outperforms unconstrained methods.
    • Achieved up to 32% reduction in coding rate for depth maps.
    • Maintained high quality for synthesized virtual views at the decoder.

    Conclusions:

    • Joint denoising and compression of depth maps is more effective than separate approaches.
    • The rate-constrained estimation framework enables efficient and high-quality 3D scene transmission.
    • This method significantly improves depth-image-based rendering functionalities.