MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception
<|reference_start|>MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception: 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.<|reference_end|>
@article{rahman2024mmvr:,
    title={MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor
  Perception},
    author={M. Mahbubur Rahman, Ryoma Yataka, Sorachi Kato, Pu Perry Wang, Peizhao
  Li, Adriano Cardace, Petros Boufounos},
    journal={arXiv preprint arXiv:2406.10708},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10708},
    primaryClass={cs.CV cs.DB eess.SP}
}