Radar Fusion
Fusion of Radar with other modalities for automotive scene understanding
Radar sensors have a significant potential for several applications due to their low-cost and robustness to weather conditions. However, their use in 3D-detection work is challenging due to the sparsity of 3D information compared to Lidar.
In our 2022 WACV paper “Fusion Point Pruning for Optimized 2D Object Detection with Radar-Camera Fusion”, we propose a method to optimize the neural network architecture for camera-radar fusion in 2D object detection:
In our ICPR 2020 publication “Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network”, we train a neural network to classify radar point cloud 3D points as normal or multi-path (ghost) detections: