REFNet++ aligns raw camera images and radar range-Doppler data into a shared bird's-eye polar view using variational encoders for multi-task vehicle detection and free space segmentation on the RADIal dataset.
RADIATE: A Radar Dataset for Automotive Perception in Bad Weather
2 Pith papers cite this work. Polarity classification is still indexing.
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Describes a camera-radar fusion network that uses raw RD spectra and BEV-polar camera features for BEV object detection, evaluated for accuracy and compute on the RADIal dataset.
citing papers explorer
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REFNet++: Multi-Task Efficient Fusion of Camera and Radar Sensor Data in Bird's-Eye Polar View
REFNet++ aligns raw camera images and radar range-Doppler data into a shared bird's-eye polar view using variational encoders for multi-task vehicle detection and free space segmentation on the RADIal dataset.
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A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data
Describes a camera-radar fusion network that uses raw RD spectra and BEV-polar camera features for BEV object detection, evaluated for accuracy and compute on the RADIal dataset.