MMVR：用于室内感知的毫米波多视角雷达数据集与基准
MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception
AI总结：
本文提出MMVR数据集，含345K多视角雷达帧、446K标注实例及759万关键点，支持室内目标检测、姿态估计和实例分割，并给出基准，以促进室内导航、能源管理和老年护理。
摘要：
与支持自动驾驶的大量汽车雷达数据集相比，室内雷达数据集规模较小，格式为低分辨率雷达点云，且通常处于开放空间单房间设置下。本文在多日、多房间、多受试者的设置下，利用多视角高分辨率雷达热图扩大了室内雷达数据采集规模，并强调环境和受试者的多样性。该数据集被称为毫米波多视角雷达（MMVR）数据集，包含从25名人类受试者在6个不同房间中收集的345K多视角雷达帧，446K个带标注的边界框/分割实例，以及7.59百万个标注关键点，分别支持目标检测、姿态估计和实例分割三大感知任务。对于每项任务，我们在两种协议下报告性能基准：开放空间中单个受试者和多个杂乱房间中的多个受试者，并采用两种数据划分：随机划分和跨环境划分，基于395个1分钟数据段。我们预计MMVR将促进室内车辆（机器人/人形）导航、建筑能源管理和老年人护理的室内雷达感知发展，以提高效率、用户体验和安全性。MMVR数据集可在https://doi.org/10.5281/zenodo.12611978获取。
英文摘要：
Compared with an extensive list of automotive radar datasets that support autonomous driving, indoor radar datasets are scarce at a smaller scale in the format of low-resolution radar point clouds and usually under an open-space single-room setting. In this paper, we scale up indoor radar data collection using multi-view high-resolution radar heatmap in a multi-day, multi-room, and multi-subject setting, with an emphasis on the diversity of environment and subjects. Referred to as the millimeter-wave multi-view radar (MMVR) dataset, it consists of $345$K multi-view radar frames collected from $25$ human subjects over $6$ different rooms, $446$K annotated bounding boxes/segmentation instances, and $7.59$ million annotated keypoints to support three major perception tasks of object detection, pose estimation, and instance segmentation, respectively. For each task, we report performance benchmarks under two protocols: a single subject in an open space and multiple subjects in several cluttered rooms with two data splits: random split and cross-environment split over $395$ 1-min data segments. We anticipate that MMVR facilitates indoor radar perception development for indoor vehicle (robot/humanoid) navigation, building energy management, and elderly care for better efficiency, user experience, and safety. The MMVR dataset is available at https://doi.org/10.5281/zenodo.12611978.
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。
