HyperLiDAR: 一种基于超维度计算的自适应部署后激光雷达分割方法
HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing
AI总结：
本文提出HyperLiDAR，一种基于超维度计算的轻量级激光雷达分割框架，解决部署后适应性问题，通过缓冲区选择策略提升效率，实现实时适应与高效率重训练。
摘要：
LiDAR语义分割在边缘应用如自动驾驶的3D场景理解中起关键作用。然而，现实部署仍面临挑战，特别是设备后部署适应性。现实环境随系统导航变化，导致性能下降。边缘系统受计算和能耗限制，无法直接适应传统分割模型。为此，我们引入HyperLiDAR，首个基于超维度计算（HDC）的轻量级部署后激光雷达分割框架。HyperLiDAR充分利用HDC的快速学习和高效性，灵感来自人脑信息处理。为进一步提高适应效率，我们识别每扫描的高数据量为瓶颈，并引入缓冲区选择策略，聚焦最信息点学习。我们在两个最先进的LiDAR分割基准和两个代表性设备上进行了广泛评估。结果表明，HyperLiDAR在适应性能上优于或达到现有最先进分割方法，同时重训练速度提升高达13.8倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
