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PRIN/SPRIN: On Extracting Point-Wise Rotation Invariant Features.

Yang You, Yujing Lou, Ruoxi Shi

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 25, 2021
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces PRIN, a novel point-set learning framework for rotation invariant point cloud analysis. Its sparse version, SPRIN, achieves superior performance in tasks like classification and segmentation without data augmentation.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • 3D Data Analysis

    Background:

    • Point cloud analysis is hindered by unknown orientations, posing challenges for real-world applications.
    • Existing methods often require pose priors or extensive data augmentation to handle orientation variations.

    Purpose of the Study:

    • To develop a novel point-set learning framework, PRIN (Point-wise Rotation Invariant Network), for robust rotation invariant feature extraction in point clouds.
    • To extend PRIN to a sparse version, SPRIN, for efficient processing of sparse point cloud data.

    Main Methods:

    • Constructing spherical signals using Density Aware Adaptive Sampling to address distorted point distributions.
    • Employing Spherical Voxel Convolution and Point Re-sampling for rotation invariant feature extraction at the point level.

    Related Experiment Videos

  • Developing SPRIN for direct operation on sparse point clouds, enhancing computational efficiency.

    • Main Results:

    • SPRIN demonstrates superior performance on randomly rotated point cloud datasets compared to state-of-the-art methods, without requiring data augmentation.
    • The proposed methods achieve point-wise rotation invariance, validated through theoretical proofs and analysis.
    • PRIN and SPRIN show versatility, applicable to object classification, part segmentation, 3D feature matching, and label alignment.

    Conclusions:

    • PRIN and SPRIN offer a powerful solution for rotation invariant point cloud analysis, overcoming limitations of existing methods.
    • The framework's ability to handle arbitrary orientations without prior information significantly advances the field.
    • Publicly available code will facilitate reproducibility and further research in 3D point cloud understanding.