The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: ACM Student Research Competition, Undergraduate
Author: Adam Niemczura (Clemson University), Max Faykus (Clemson University), Oyinlolu Odetoye (Clemson University), Melissa Smith (Clemson University), Jon Calhoun (Clemson University), Scott Groel (Clemson University)
Supervisor: Melissa Smith (Clemson University)
Abstract: Transmitting point cloud data is vital for applications like autonomous vehicle navigation, especially for compute-limited vehicles. LiDAR data can easily grow to gigabytes or terabytes uncompressed, making data transmission costly. While recent research has advanced point cloud compression, most work evaluates performance using object detection and on urban datasets like SemanticKITTI [1] or nuScenes [2]. These do not exactly reflect performance on off-road outdoor data, which is typically noisier and less structured. We benchmark three LiDAR compressors, RENO (neural-based) [8], TMC13 (rules-based baseline) [5], and LCP [9] (scientific particle compressor untested in this domain) on the GOOSE dataset [6]. We trained two 3D semantic segmentation models on this decompressed LiDAR data to observe their downstream segmentation performance. Ultimately, we find RENO to outperform TMC13 and LCP, with LCP providing competitive results to RENO and TMC13 in compression quality and speeds.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF