  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.
