Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences

TL;DR

The paper introduces 'Jiffy', a fast lossless compression algorithm for LiDAR data that exploits spatiotemporal redundancy and SIMD instructions, achieving high compression ratios and speeds suitable for real-time applications.

Contribution

It presents a novel SIMD-accelerated lossless compression method for LiDAR data that outperforms existing codecs in speed and compression ratio, with open-source implementation.

Findings

Achieves over 65 million points/sec compression speed.

Provides 6x compression for centimeter-precision scans in autonomous driving.

Outperforms competing lossless codecs in benchmarks.

Abstract

As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour and are often used in applications with limited compute, bandwidth, and storage resources. We present a fast, lossless compression algorithm for LiDAR range and attribute scan sequences including multiple-return range, signal, reflectivity, and ambient infrared. Our algorithm -- dubbed "Jiffy" -- achieves substantial compression by exploiting spatiotemporal redundancy and sparsity. Speed is accomplished by maximizing use of single-instruction-multiple-data (SIMD) instructions. In autonomous driving, infrastructure monitoring, drone inspection, and handheld mapping benchmarks, the Jiffy algorithm consistently outcompresses competing lossless codecs while operating at speeds in excess of 65M points/sec on a single core. In a typical autonomous vehicle use case, single-threaded Jiffy achieves 6x compression of centimeter-precision range scans at 500+ scans per second. To ensure reproducibility and enable adoption, the software is freely available as an open source library.