通过事件相机与激光雷达融合，实现动态自适应深度感知。
NeuroLiDAR: Adaptive Frame Rate Depth Sensing via Neuromorphic Event-LiDAR Fusion
- 用事件相机触发关键帧，动态调节激光雷达采样频率。
- 深度重建误差降低约29%，帧率可达27.8-47.3 Hz。
- 适合需要低延迟高精度的自动驾驶与机器人应用。
激光雷达广泛用于三维深度重建，但其性能受限于硬件约束，常在测距、空间分辨率和帧率间权衡。多数系统帧率较低（如5-10 Hz），侧重远距离感知而非快速响应场景变化。本文提出NeuroLiDAR，一种自适应深度感知框架，通过融合时间稀疏的激光雷达数据与时间密集的类脑事件相机输入，实现约66 Hz的有效帧率。该框架包含事件驱动的关键帧检测与事件引导的深度外推两部分，能根据场景动态调整感知速率。为评估方法，我们构建了涵盖室内外场景的ELiDAR数据集，并验证NeuroLiDAR在均方根误差（RMSE）上降低约29%，同时实现27.8–47.3 Hz的自适应帧率。代码与数据集已开源。
原文摘要 · Abstract (English)
LiDARs are widely used for 3D depth reconstruction, but their performance is often limited by inherent hardware constraints that impose trade-offs between range, spatial resolution, and frame rate. Many LiDAR systems typically operate at low frame rates (e.g., 5-10 Hz), prioritizing long-range sensing over responsiveness to rapid scene changes. We present NeuroLiDAR, an adaptive depth sensing framework that achieves effective frame rates of up to $\approx$66 Hz by fusing temporally sparse LiDAR data with temporally dense inputs from neuromorphic event cameras. NeuroLiDAR integrates two components: event-based keyframe detection and event-guided depth extrapolation, to dynamically adjust the sensing rate in response to scene dynamics. To evaluate our approach, we introduce ELiDAR, a dataset spanning outdoor and indoor scenarios, and show that NeuroLiDAR reduces depth reconstruction error by $\approx$29\% in RMSE while achieving adaptive frame rates between 27.8-47.3 Hz. Our code and dataset are available at https://github.com/darshanakgr/neurolidar.
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