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In today's era, three-dimensional point cloud data is not only voluminous but also widely applicable. Therefore, data compression has become a crucial step prior to processing. Although existing 3D point cloud compression techniques primarily focus on fidelity, in practical applications, the vast majority of compressed data serves machine perception tasks. Therefore, point cloud compression tailored for machine perception becomes particularly significant. To address this problem, we introduce an innovative point cloud compression algorithm library specifically designed for both machine and human perceptual requirements. This library represents the first collection of multi-perception point cloud compression algorithms on the PyTorch platform, integrating eleven advanced, learning-based algorithms. We category and analyze these algorithms in depth, according to different analysis tasks, to facilitate a better understanding and comparison. Moreover, we successfully replicate these algorithms and meticulously organize the pre-processing of point cloud data and the analysis networks for downstream tasks. Ultimately, we conduct experiments on multiple perceptual datasets for compression and analysis tasks, with results comprehensively summarized across various performance metrics. We will continue to update these algorithms to ease their adoption by researchers.
Xie et al. (Sat,) studied this question.