Presentation
Evaluating LiDAR Compression for 3D Semantic Segmentation in Diverse Off-Road Environments on GOOSE Dataset
DescriptionTransmitting 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.
Event Type
Best Poster Presentations (Research, ACM SRC Grad/Undergrad)
TimeWednesday, 19 November 20251:45pm - 2:00pm CST
Location230
Archive
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