MilliNoise: a Millimeter-wave Radar Sparse Point Cloud Dataset in Indoor Scenarios - Walter Brescia; Pedro Gomes; Laura Toni; Saverio Mascolo; Luca De Cicco (Proceedings of the 15th ACM Multimedia Systems Conference, 2024)
Millimeter-wave (mmWave) radar sensors produce Point Clouds (PCs) that are much sparser and noisier than other PC data (e.g., Li-DAR), yet they are more robust in challenging conditions such as in the presence of fog, dust, smoke, or rain. This paper presents MilliNoise, a point cloud dataset captured in indoor scenarios through a mmWave radar sensor installed on a wheeled mobile robot. Each of the 12M points in the MilliNoise dataset is accurately labeled as true/noise point by leveraging known information of the scenes and a motion capture system to obtain the ground truth position of the moving robot. Each frame is carefully pre-processed to produce a fixed number of points for each cloud, enabling classification tools which require data with a fixed shape. Moreover, MilliNoise has been post-processed by labeling each point with the distance to its closest obstacle in the scene, which allows casting the denoising task into the regression framework. Along with the dataset, we provide researchers with the tools to visualize the data and prepare it for statistical and machine learning analysis. MilliNoise is available at: https://github.com/c3lab/MilliNoise