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Non-Rigid Shape From Water.

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    This study presents a new 3D sensing technique for detailed shape recovery of dynamic, non-rigid objects underwater. The method effectively reconstructs full 3D surfaces, even for moving objects, using advanced scene flow estimation and near-infrared imaging.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Optical Sensing

    Background:

    • Accurate 3D shape reconstruction of dynamic, non-rigid objects in underwater environments is challenging.
    • Existing methods often struggle with occlusion, deformation, and water's optical properties.

    Purpose of the Study:

    • To develop a novel 3D sensing method for recovering consistent, dense 3D shapes of dynamic, non-rigid objects in water.
    • To reconstruct a complete 3D surface in a canonical frame from freely deforming and moving objects.

    Main Methods:

    • Estimating underwater 3D scene flow to integrate per-frame depth.
    • Utilizing two near-infrared observations for depth estimation.
    • Leveraging geometric and radiometric constraints for shape refinement.
    • Implementing a practical calibration method for a single camera and light source, accounting for scattering.

    Main Results:

    • Demonstrated effective 3D shape reconstruction of static, deformable, and dynamic objects and creatures in real-world water.
    • Successfully recovered complete shapes of complex, non-rigid objects.
    • Achieved video-rate imaging system prototype.

    Conclusions:

    • The novel method enables robust 3D shape recovery for dynamic, non-rigid underwater objects.
    • Opens new applications for underwater 3D sensing in the sub-meter range.