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ADCNet: Learning from Raw Radar Data via Distillation
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ADCNet: Learning from Raw Radar Data via Distillation
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As autonomous vehicles and advanced driving assistance systems have entered wider deployment, there is an increased interest in building robust perception systems using radars. Radar-based systems are lower cost and more robust to adverse weather conditions than their LiDAR-based counterparts; however the point clouds produced are typically noisy and sparse by comparison. In order to combat these challenges, recent research has focused on consuming the raw radar data, instead of the final radar point cloud. We build on this line of work and demonstrate that by bringing elements of the signal processing pipeline into our network and then pre-training on the signal processing task, we are able to achieve state of the art detection performance on the RADIal dataset. Our method uses expensive offline signal processing algorithms to pseudo-label data and trains a network to distill this information into a fast convolutional backbone, which can then be finetuned for perception tasks. Extensive experiment results corroborate the effectiveness of the proposed techniques.
Forward citations
Cited by 5 Pith papers
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DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather
DinoRADE reports a radar-centered multi-class detection pipeline that fuses dense radar tensors with DINOv3 features via deformable attention and outperforms prior radar-camera methods by 12.1% on the K-Radar dataset ...
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DeeperRadar: End-to-End MIMO Radar Design and Multi-Modal Fusion for Autonomous Vehicle Perception
Learning which MIMO radar receivers to activate jointly with camera–LiDAR fusion lets fewer receivers match or exceed full-array 3D detection on RADIal, with the best budget depending on the sensor stack.
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RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation
RAVEN introduces a chirp-wise streaming radar perception network with MIMO-preserving encoders, learnable cross-antenna mixing, and early-exit to deliver competitive detection and BEV segmentation at reduced compute a...
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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.
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