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Related Experiment Videos

Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression.

Chang Sun, Hui Yuan, Shiqi Jiang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 29, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces Inter-LPCM, a novel learning-based method for compressing LiDAR point clouds. It significantly improves compression efficiency by leveraging inter-frame correlations, outperforming existing methods in rate-distortion performance.

    Area of Science:

    • Computer Vision
    • Signal Processing
    • Data Compression

    Background:

    • LiDAR point clouds are efficiently compressed using spherical coordinates.
    • Existing methods like PredGeom have limitations in capturing complex motion and inter-frame correlations.
    • Learning-based methods in the spherical domain often neglect inter-frame redundancy.

    Purpose of the Study:

    • To develop a learning-based inter-frame predictive coding method (Inter-LPCM) for LiDAR point clouds.
    • To enhance compression efficiency by exploiting inter-frame correlations in the spherical domain.
    • To improve upon the rate-distortion performance of existing point cloud compression techniques.

    Main Methods:

    • Proposed Inter-LPCM utilizes delta coding for azimuth prediction.

    Related Experiment Videos

  • Introduced an inter-frame radius predictive (Inter-RP) model for radius compression.
  • Designed a lightweight attention-based prediction (LAEP) model for elevation angle prediction.
  • Implemented RD-optimized quantization and distinct entropy coding models for spherical components.

    • Main Results:

    • Inter-LPCM achieved a 26.1% BD-rate reduction compared to G-PCC lossless octree coding on SemanticKITTI.
    • Demonstrated an 8.3% BD-rate reduction compared to PredGeom's inter-frame mode on Ford dataset.
    • The method effectively captures long-range geometric correlations and exploits inter-frame redundancies.

    Conclusions:

    • Inter-LPCM offers superior rate-distortion performance for LiDAR point cloud compression.
    • The proposed method advances the state-of-the-art in spherical domain point cloud compression.
    • Publicly available source code facilitates further research and development.