A frequency-domain variation modeling framework regularizes multi-modal BEV features to improve cross-dataset radar-camera 3D detection without target-domain samples during training.
A survey of deep learning based radar and vision fusion for 3d object detection in autonomous driving
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
3DTMDet proposes a hybrid Mamba-Transformer architecture with a 3DHMT block and LiDAR-inspired voxel generation to improve 3D object detection in point clouds, outperforming prior methods on KITTI and ONCE datasets.
Radar-scoped edge density gate verifies obstacles in radar ROIs for AEB without training or GPU, reducing search space 98.7% with 0.994 recall and zero missed brakes across 33 scenarios on real vehicle.
citing papers explorer
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Understanding Cross-Sensor Feature Variations for Generalizable 3D Perception
A frequency-domain variation modeling framework regularizes multi-modal BEV features to improve cross-dataset radar-camera 3D detection without target-domain samples during training.
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3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds
3DTMDet proposes a hybrid Mamba-Transformer architecture with a 3DHMT block and LiDAR-inspired voxel generation to improve 3D object detection in point clouds, outperforming prior methods on KITTI and ONCE datasets.
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Radar Guided Camera Verification for Automatic Emergency Braking Rethinking Object Detection in Radar Camera Fusion
Radar-scoped edge density gate verifies obstacles in radar ROIs for AEB without training or GPU, reducing search space 98.7% with 0.994 recall and zero missed brakes across 33 scenarios on real vehicle.