AevaScenes：用于远距离感知的FMCW激光雷达数据集与基准
AevaScenes: An FMCW LiDAR Dataset and Benchmark for Long-Range Perception
AI总结：
提出FMCW激光雷达远距离感知数据集AevaScenes及三项基准任务，验证多普勒测量可显著提升远距离检测与场景流精度。
AI中文摘要：
FMCW激光雷达在测量距离的同时，可测量每个点的径向多普勒速度，提供了传统飞行时间传感器所不具备的运动线索。在远距离场景下利用该信号的研究仍不充分。我们提出了一个FMCW激光雷达数据集，包含575个序列（57.5K帧），超过800万个标注的3D边界框，覆盖16个检测类别，以及24个语义类别的逐点标签。数据由六台商用FMCW激光雷达传感器和六对4K相机在湾区八个城市采集，其中包括237个夜间序列，标注范围延伸至400米。我们定义了一个包含三项任务的基准：3D物体检测、场景流估计和语义分割。检测和场景流任务在三个距离区间内评估至400米，并提供公开评估服务器。我们探讨了多普勒测量对旗舰识别任务的影响，发现远距离车辆和行人的检测AP显著提升，最高可达2倍，尤其在低延迟单帧设置中。同样，我们发现场景流精度在所有距离区间内均因多普勒测量而显著提高。我们的数据集和基准已在以下网址公开发布：此https URL。
英文摘要：
FMCW LiDAR measures per-point radial Doppler velocity alongside range, providing a motion cue unavailable in conventional time-of-flight sensors. Exploiting this signal at long range remains understudied. We present an FMCW LiDAR dataset of 575 sequences (57.5K frames) with over 8 million annotated 3D boxes across 16 detection classes and per-point labels across 24 semantic classes, captured by six commercial FMCW LiDAR sensors and six paired 4K cameras across eight Bay Area cities, including 237 nighttime sequences, with annotations extending to 400m. We define a benchmark with three tasks: 3D object detection, scene flow estimation, and semantic segmentation. Detection and scene flow are evaluated across three range bins to 400m, with a public evaluation server. We explore the impact of Doppler measurements on flagship recognition tasks, and find significant improvements up to 2X in detection AP of far-away vehicles and pedestrians, particularly in low-latency single-frame settings. We similarly find scene flow accuracy is significantly improved with Doppler measurements across all ranges. Our dataset and benchmark have been publicly released at https://scenes.aeva.com.
