Radar object detection improves by fusing features learned from radar-camera pairs with semantic-depth maps from 3D city models, demonstrated on a new 54K-pair Munich dataset.
Self- supervised learning for domain adaptation on point clouds
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RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning
Radar object detection improves by fusing features learned from radar-camera pairs with semantic-depth maps from 3D city models, demonstrated on a new 54K-pair Munich dataset.