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DTSTAMP:20260202T201247Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20251120T080000
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UID:submissions.supercomputing.org_SC25_sess533_post223@linklings.com
SUMMARY:Mitigating I/O Bottlenecks in LiDAR Pipelines by Directly Merging 
 Neural Decompression and Semantic Segmentation
DESCRIPTION:Ethan Marquez, Max Faykus, Oyinlolu Odetoye, Melissa Smith, an
 d Jon Calhoun (Clemson University)\n\nThe increasing volume of high-resolu
 tion 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 add
 resses this issue by proposing the first unified RENO-based neural autoenc
 oder with a Point Transformer v3 (PTV3) segmentation backbone. This integr
 ated 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 Ger
 man Outdoor and Offroad (GOOSE) dataset, this approach enables direct sema
 ntic analysis on compressed data. Our results demonstrate that this method
  significantly reduces storage overhead, saving 29.9 GB per 13,076 point c
 louds and 2.7 GB per minute of LiDAR operation, all while maintaining the 
 accuracy of semantic segmentation. This unified framework represents a maj
 or step towards efficient, real-time processing of large-scale point cloud
  datasets.\n\nTag: Research & ACM SRC Posters\n\nRegistration Category: Te
 chnical Program Reg Pass\n\nSession Chairs: Kento Sato (RIKEN Center for C
 omputational Science (R-CCS)); Chris Schlipalius (Pawsey Supercomputing Re
 search Centre; Commonwealth Scientific and Industrial Research Organisatio
 n (CSIRO), Australia); and Anja Gerbes (Georg-August-Universität Göttingen
 )\n\n
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