Software & Data Downloads — MMVR

Compared with an extensive range of automotive radar datasets to support autonomous driving, indoor radar datasets are scarce at a much smaller scale in the format of low-resolution radar point clouds and usually under an open-space single-room setting. In this paper, we aim to scale up indoor radar data collection in a large-scale, multi-view high-resolution heatmap in a multi-day, multi-room, and multi-subject setting. Referring to the millimeter-wave multi-view radar (MMVR) dataset, it consists of $345$K multi-view radar heatmap frames collected from $22$ human subjects over $6$ different rooms (e.g, open/cluttered offices and meeting rooms). Each pair of horizontal and vertical radar frames is synchronized with RGB image-plane annotations: bounding boxes, keypoints, and pixel-level occupancy that support three major perception tasks of object detection, pose estimation, and 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 sequences. We anticipate that MMVR facilitates radar-assisted indoor perception development for indoor vehicle/robot/humanoid navigation, building energy management, and elderly care for better efficiency, safety, and user experience.

Access data at https://doi.org/10.5281/zenodo.12611978.