CLLAP generates LiDAR-based pseudo-radar data and applies dual-modality contrastive pretraining to boost radar-camera fusion models for 3D detection, showing gains on NuScenes and Lyft datasets.
Bevcar: Camera-radar fusion for bev map and object segmentation
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2representative citing papers
CTAB bidirectional deformable attention between detection and segmentation BEV branches improves 7-class mIoU by 0.6 at neutral detection on nuScenes radar-camera multi-task learning.
citing papers explorer
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CLLAP: Contrastive Learning-based LiDAR-Augmented Pretraining for Enhanced Radar-Camera Fusion
CLLAP generates LiDAR-based pseudo-radar data and applies dual-modality contrastive pretraining to boost radar-camera fusion models for 3D detection, showing gains on NuScenes and Lyft datasets.
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Radar-Camera BEV Multi-Task Learning with Cross-Task Attention Bridge for Joint 3D Detection and Segmentation
CTAB bidirectional deformable attention between detection and segmentation BEV branches improves 7-class mIoU by 0.6 at neutral detection on nuScenes radar-camera multi-task learning.