用序列化方法压缩激光雷达反射率数据，效率远超现有技术。
Efficient LiDAR Reflectance Compression via Scanning Serialization
- 将点云按扫描顺序转为一维序列，用位置、距离和历史反射率建模上下文。
- 压缩后体积减少2倍以上，比特数比顶尖方法低22%。
- 参数量仅需2%，轻量版每秒处理超10帧，适合实际部署。
激光雷达点云中的反射率属性对下游任务至关重要，但在神经压缩方法中仍研究不足。为此，我们提出SerLiC，一种基于序列化的神经压缩框架，充分挖掘激光雷达反射率的内在特性。SerLiC首先通过扫描顺序序列化将三维点云转化为一维序列，提供面向设备的反射率分析视角。每个点被编码为包含传感器扫描序号、径向距离和先前反射率的上下文表示，以有效探索依赖关系。为实现高效序列建模，引入Mamba并采用双并行化方案，可同时捕捉自回归依赖与快速处理。大量实验表明，SerLiC在压缩后体积减少超过2倍，相比当前最优方法比特数降低高达22%，且仅使用其2%的参数。此外，轻量版SerLiC仅需111K参数即可实现超过10 fps的处理速度，适用于真实场景应用。
原文摘要 · Abstract (English)
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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