New LiDAR compression method uses Mamba for efficient data reduction

Researchers have developed SerLiC, a novel neural compression framework designed to efficiently compress LiDAR reflectance data. This method serializes 3D LiDAR point clouds into 1D sequences, enabling better analysis of reflectance attributes. By incorporating Mamba with a dual parallelization scheme, SerLiC achieves significant volume reduction, outperforming existing state-of-the-art methods while requiring fewer parameters. A lightweight version of SerLiC demonstrates real-time processing capabilities, making it suitable for practical applications. AI

IMPACT This compression technique could enable more efficient processing and storage of sensor data for AI applications.

RANK_REASON The cluster contains an academic paper detailing a new method for data compression. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• Jiahao Zhu
• LiDAR
• Mamba

• paper
• infra

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