PreSight：利用城市级NeRF先验增强自动驾驶感知
PreSight: Enhancing Autonomous Vehicle Perception with City-Scale NeRF Priors
AI总结：
针对自动驾驶感知在遮挡、极端光照等场景下的挑战，提出PreSight框架，利用城市级NeRF构建静态先验记忆增强在线感知，在nuScenes数据集的高精地图构建和占用预测任务上取得显著提升。
摘要：
自动驾驶车辆高度依赖感知系统实现导航并解读周围环境。尽管近期这类系统取得了显著进展，但在遮挡、极端光照或陌生城区等条件下仍存在挑战。与这些系统不同，人类并非仅依靠即时观测来感知环境。在陌生城市导航时，人类会逐步形成初步的心理地图，在后续到访时补充实时感知。受人类这一行为启发，我们提出了一种新型框架PreSight，它利用过往行驶轨迹构建静态先验记忆，以增强后续导航中的在线感知能力。我们的方法包括用过往行程的数据优化一个城市级神经辐射场（NeRF），以生成神经先验。这些先验富含语义和几何细节，无需人工标注即可生成，并且能够无缝适配多种前沿感知模型，以极低的额外计算成本提升模型效能。在nuScenes数据集上的实验结果表明，该框架与各类在线感知模型具有高度兼容性。具体而言，它在高精地图构建和占用预测任务上展现出显著提升，凸显了其作为自动驾驶系统新型感知框架的潜力。我们的代码将发布于https://github.com/yuantianyuan01/PreSight。
英文摘要：
Autonomous vehicles rely extensively on perception systems to navigate and interpret their surroundings. Despite significant advancements in these systems recently, challenges persist under conditions like occlusion, extreme lighting, or in unfamiliar urban areas. Unlike these systems, humans do not solely depend on immediate observations to perceive the environment. In navigating new cities, humans gradually develop a preliminary mental map to supplement real-time perception during subsequent visits. Inspired by this human approach, we introduce a novel framework, PreSight, that leverages past traversals to construct static prior memories, enhancing online perception in later navigations. Our method involves optimizing a city-scale neural radiance field with data from previous journeys to generate neural priors. These priors, rich in semantic and geometric details, are derived without manual annotations and can seamlessly augment various state-of-the-art perception models, improving their efficacy with minimal additional computational cost. Experimental results on the nuScenes dataset demonstrate the framework's high compatibility with diverse online perception models. Specifically, it shows remarkable improvements in HD-map construction and occupancy prediction tasks, highlighting its potential as a new perception framework for autonomous driving systems. Our code will be released at https://github.com/yuantianyuan01/PreSight.
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。
