Presentation
Hyperlidar: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing
DescriptionLiDAR semantic segmentation plays a pivotal role in scene understanding for edge applications such as autonomous driving. Once deployed in the real world, the model must adapt to its surrounding environment through rapid adaptive training and updates, even with limited compute and energy resources
on an edge device. Existing segmentation models rely on large neural networks, which need significant memory and computing resources for post-deployment adaptation. To address the above challenges, we introduce HyperLiDAR, the first lightweight LiDAR segmentation framework based on Hyperdimensional
Computing (HDC) for adapting to streaming point cloud scans after deployment. HDC is a brain-inspired approach well-suited for efficient on-device learning. HyperLiDAR combines a pretrained feature extractor with HDC training to support lightweight adaptation on edge devices. We further design a buffer selection
strategy to mitigate the high data volume in each scan. Extensive evaluations on two state-of-the-art LiDAR segmentation benchmarks and three representative devices demonstrate that HyperLiDAR outperforms state-of-the-art segmentation methods and accelerates retraining speed by up to 13.8×.
on an edge device. Existing segmentation models rely on large neural networks, which need significant memory and computing resources for post-deployment adaptation. To address the above challenges, we introduce HyperLiDAR, the first lightweight LiDAR segmentation framework based on Hyperdimensional
Computing (HDC) for adapting to streaming point cloud scans after deployment. HDC is a brain-inspired approach well-suited for efficient on-device learning. HyperLiDAR combines a pretrained feature extractor with HDC training to support lightweight adaptation on edge devices. We further design a buffer selection
strategy to mitigate the high data volume in each scan. Extensive evaluations on two state-of-the-art LiDAR segmentation benchmarks and three representative devices demonstrate that HyperLiDAR outperforms state-of-the-art segmentation methods and accelerates retraining speed by up to 13.8×.
Event Type
Research Manuscript
TimeTuesday, July 2812:03pm - 12:16pm PDT
LocationMtg Room 202C