The rapid advancements in machine learning and computer vision have transformed the way signals and images are processed, opening new opportunities in areas such as autonomous driving, human–machine interaction, healthcare, and surveillance. Among the sensing technologies enabling these applications, millimeter-wave radar has recently gained increasing attention. Compared to cameras and LiDARs, radar sensors are low-cost, robust to adverse weather conditions, and inherently privacy-preserving. Their relevance is further amplified by the forthcoming 6G integrated sensing and communications (ISAC) paradigm, where sensing and wireless connectivity will converge into a unified infrastructure. Despite these advantages, radar data remain challenging to process: they are low-resolution, sparse, and noisy.