Efficient LiDAR Reflectance Compression via Scanning Serialization

• URL: http://arxiv.org/abs/2505.09433v2
• Date: Tue, 27 May 2025 05:54:18 GMT
• Title: Efficient LiDAR Reflectance Compression via Scanning Serialization
• Authors: Jiahao Zhu, Kang You, Dandan Ding, Zhan Ma,
• Abstract summary: SerLiC is a serialization-based neural compression framework for reflectance analysis.<n>It attains over 2x volume reduction against the original reflectance data.<n>A lightweight version of SerLiC achieves > 10 fps (frames per second) with just 111K parameters.
• Score: 19.257711579886006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reflectance attributes in LiDAR point clouds provide essential information for downstream tasks but remain underexplored in neural compression methods. To address this, we introduce SerLiC, a serialization-based neural compression framework to fully exploit the intrinsic characteristics of LiDAR reflectance. SerLiC first transforms 3D LiDAR point clouds into 1D sequences via scan-order serialization, offering a device-centric perspective for reflectance analysis. Each point is then tokenized into a contextual representation comprising its sensor scanning index, radial distance, and prior reflectance, for effective dependencies exploration. For efficient sequential modeling, Mamba is incorporated with a dual parallelization scheme, enabling simultaneous autoregressive dependency capture and fast processing. Extensive experiments demonstrate that SerLiC attains over 2x volume reduction against the original reflectance data, outperforming the state-of-the-art method by up to 22% reduction of compressed bits while using only 2% of its parameters. Moreover, a lightweight version of SerLiC achieves > 10 fps (frames per second) with just 111K parameters, which is attractive for real-world applications.

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