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.

本文介绍了 MMVR 数据集，它是一个多视角、高分辨率的毫米波多视角雷达数据集，包含了来自 25 个人在 6 个不同房间收集的 345,000 个多视角雷达帧、446,000 个标注的边界框 / 分割实例和 7,590,000 个标注的关键点，以支持三个主要感知任务：目标检测、姿态估计和实例分割。在一片开放空间中的单一主体和多个拥挤房间中的多个主体上，我们报告了两个协议下的性能指标，并分别采用了随机分割和跨环境分割这两种数据划分策略。我们期望 MMVR 数据集能够促进室内雷达感知在室内车辆（机器人 / 仿人）导航、建筑能源管理和老年人护理等领域的发展，以提高效率、用户体验和安全性。