A Regularized Projection-Based Geometry Compression Scheme for LiDAR Point Cloud

Yu, Youguang; Zhang, Wei; Li, Ge; Yang, Fuzheng

doi:10.1109/tcsvt.2022.3211084

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…Several research studies [58,64,65] have explored the utilization of PNG, JPEG, and their variations for compressing LiDAR data, comparing their results with other image-based and non-image-based compression algorithms to showcase the applicability of these approaches with LiDAR sensors. Aiming at enhancing the compression ratios, Youguang et al [66] studied the Cartesian-to-cylindrical projection as an alternative to the Cartesian and spherical coordinate systems to represent the LiDAR point cloud. Their proposal includes a regularized representation considering the LiDAR's mechanical structure and acquisition pattern, specially tailored to LiDAR data compression.…”

Section: Intra-frame Compressionmentioning

confidence: 99%

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

Roriz,

Silva,

Dias

et al. 2024

Sensors

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In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics.

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