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Inter-LPCM: Learning-Based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression
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DOI：10.1109/TIP.2026.3706296.png)
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Abstract
En 中文
Because LiDAR sensors acquire point clouds with a fixed angular resolution, the resulting data can be systematically parameterized and efficiently compressed in the spherical coordinate system. Traditional spherical coordinate-based point cloud compression methods have shown strong rate-distortion (RD) performance, with the predictive geometry coding (PredGeom) method in the geometry-based point cloud compression (G-PCC) standard being a prominent example. While PredGeom includes an inter-frame prediction mode, it relies on a simple linear model, which limits its ability to capture complex motion patterns or structural dependencies. On the other hand, existing learning-based compression methods in the spherical domain do not exploit inter-frame correlations to reduce geometry redundancy. To address these limitations, we propose a learning-based inter-frame predictive coding method (Inter-LPCM). For azimuth prediction, we use a delta coding strategy based on the predefined angular resolution. To improve compression for radii, we introduce an inter-frame radius predictive (Inter-RP) model that estimates the current point’s radius using neighboring points from both the current frame and the registered reference frame. In addition, we design a lightweight attention-based prediction (LAEP) model to predict elevation angles by capturing long-range geometric correlations across different coordinates. For quantization, we propose an RD-optimized method to select the quantization steps in the spherical coordinate system. For entropy coding, we design distinct models for each spherical coordinate component. These models are adapted to the statistical priors of each coordinate, which enables more accurate probability estimation. Experimental results show that Inter-LPCM, in its best RD configuration, achieved a D1-PSNR BD-rate reduction of 26.1% compared with the G-PCC lossless octree-based coding mode on SemanticKITTI, and 8.3% compared with the inter-frame prediction mode of PredGeom on Ford, using the latest G-PCC test model TMC13 v31.0. Our source code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SDUChangSun/Inter-LPCM</uri>
Keywords:
Point cloud compression
predictive geometry coding
rate-distortion optimization
deep learning
Journal
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13.7
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8.4W