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Found 4 papers, 1 papers with code
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features
The inherent noisy and sparse characteristics of radar data pose challenges in finding effective representations for 3D object detection.
PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network
The PillarGen model performs the following three steps: 1) pillar encoding, 2) Occupied Pillar Prediction (OPP), and 3) Pillar to Point Generation (PPG).
RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection
While LiDAR sensors have been successfully applied to 3D object detection, the affordability of radar and camera sensors has led to a growing interest in fusing radars and cameras for 3D object detection.
Ranked #4 on
3D Object Detection
on nuscenes Camera-Radar
3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection
First, the method employs auto-calibrated projection, to transform the 2D camera features to a smooth spatial feature map with the highest correspondence to the LiDAR features in the bird's eye view (BEV) domain.
