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Topology-Aware Non-Rigid Point Cloud Registration.

Konstantinos Zampogiannis, Cornelia Fermuller, Yiannis Aloimonos

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 13, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel non-rigid registration pipeline for point clouds with differing topologies. By utilizing backward motion and a hybrid approach, it accurately estimates motion across topological changes, improving registration accuracy.

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    Area of Science:

    • Computer Vision
    • Geometric Processing
    • 3D Reconstruction

    Background:

    • Non-rigid registration of point clouds is challenging, especially with topological differences.
    • Standard methods struggle with motion estimation at boundaries of 'close-to-open' topology changes.

    Purpose of the Study:

    • To develop a robust non-rigid registration pipeline for topologically distinct point clouds.
    • To improve motion estimation accuracy on boundaries with topological changes.

    Main Methods:

    • Exploiting backward motion to correctly handle 'close-to-open' topology changes.
    • Employing a topology-agnostic warp field estimation algorithm.
    • Implementing a post-processing phase to detect and blend motion hypotheses based on topological events (contacts/separations).

    Main Results:

    • Achieved state-of-the-art motion estimation accuracy on the MPI Sintel dataset.
    • Demonstrated effectiveness in estimating motion on event boundaries using a custom dataset.
    • Showcased promising performance in explicit topological event detection.

    Conclusions:

    • The proposed pipeline effectively addresses challenges in non-rigid registration of point clouds with topological variations.
    • The backward motion and event-aware blending strategy significantly enhance motion estimation accuracy.
    • The method shows potential for accurate topological event detection in 3D data.