HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing

2026-04-14

AI Summary

This work addresses the performance degradation of LiDAR semantic segmentation models on edge devices under environmental shifts and their limited capacity for online adaptation. To this end, we propose the first lightweight online self-adaptation framework based on Hyperdimensional Computing (HDC). Our approach integrates an information-driven strategy for selecting key points into a buffered replay set, enabling efficient post-deployment learning while substantially reducing computational and energy overhead. Experimental results demonstrate that the proposed method achieves adaptation accuracy on par with or superior to state-of-the-art approaches across two mainstream LiDAR segmentation benchmarks, while accelerating retraining by up to 13.8× on representative edge hardware.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

LiDAR semantic segmentation plays a pivotal role in 3D scene understanding for edge applications such as autonomous driving. However, significant challenges remain for real-world deployments, particularly for on-device post-deployment adaptation. Real-world environments can shift as the system navigates through different locations, leading to substantial performance degradation without effective and timely model adaptation. Furthermore, edge systems operate under strict computational and energy constraints, making it infeasible to adapt conventional segmentation models (based on large neural networks) directly on-device. To address the above challenges, we introduce HyperLiDAR, the first lightweight, post-deployment LiDAR segmentation framework based on Hyperdimensional Computing (HDC). The design of HyperLiDAR fully leverages the fast learning and high efficiency of HDC, inspired by how the human brain processes information. To further improve the adaptation efficiency, we identify the high data volume per scan as a key bottleneck and introduce a buffer selection strategy that focuses learning on the most informative points. We conduct extensive evaluations on two state-of-the-art LiDAR segmentation benchmarks and two representative devices. Our results show that HyperLiDAR outperforms or achieves comparable adaptation performance to state-of-the-art segmentation methods, while achieving up to a 13.8x speedup in retraining.

Problem

• LiDAR segmentation
• post-deployment adaptation
• edge computing
• domain shift
• on-device learning

Innovation

• Hyperdimensional Computing
• LiDAR segmentation
• post-deployment adaptation
• edge computing
• buffer selection