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: Ethan Marquez (Clemson University), Max Faykus (Clemson University), Oyinlolu Odetoye (Clemson University), Melissa Smith (Clemson University), Jon Calhoun (Clemson University)
Supervisor: Melissa Smith (Clemson University)
Abstract: The increasing volume of high-resolution LiDAR data poses a significant I/O bottleneck in large-scale analysis and high-performance computing pipelines due to costly intermediary data storage and retrieval. We introduce a novel, end-to-end framework that addresses this issue by proposing the first unified RENO-based neural autoencoder with a Point Transformer v3 (PTV3) segmentation backbone. This integrated architecture directly feeds the high rank feature tensors of the RENO decoder into the segmentation backbone, completely bypassing the need for costly intermediary file storage and I/O operations. Evaluated on the German Outdoor and Offroad (GOOSE) dataset, this approach enables direct semantic analysis on compressed data. Our results demonstrate that this method significantly reduces storage overhead, saving 29.9 GB per 13,076 point clouds and 2.7 GB per minute of LiDAR operation, all while maintaining the accuracy of semantic segmentation. This unified framework represents a major step towards efficient, real-time processing of large-scale point cloud datasets.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF