ConFusion reaches 59.1 mAP and 65.6 NDS on nuScenes validation by combining heterogeneous queries with QMix cross-attention and QSwap feature exchange.
Bevfusion4d: Learning lidar-camera fusion under bird’s-eye-view via cross-modality guidance and temporal aggregation
4 Pith papers cite this work. Polarity classification is still indexing.
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RayMamba improves long-range 3D object detection by ray-aligned serialization of sparse voxels for state space modeling, delivering up to 2.49 mAP gain on nuScenes in the 40-50 m range.
DualViewMapDet fuses prior-traversal point cloud maps into camera features via dual perspective-view and bird's-eye-view encoding to improve 3D detection and tracking without LiDAR.
Co-Fusion4D introduces current-frame-centric spatiotemporal fusion and dual attention to reach 74.9% mAP and 75.6% NDS on nuScenes for 3D detection.
citing papers explorer
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Control Your Queries: Heterogeneous Query Interaction for Camera-Radar Fusion
ConFusion reaches 59.1 mAP and 65.6 NDS on nuScenes validation by combining heterogeneous queries with QMix cross-attention and QSwap feature exchange.
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RayMamba: Ray-Aligned Serialization for Long-Range 3D Object Detection
RayMamba improves long-range 3D object detection by ray-aligned serialization of sparse voxels for state space modeling, delivering up to 2.49 mAP gain on nuScenes in the 40-50 m range.
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Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking
DualViewMapDet fuses prior-traversal point cloud maps into camera features via dual perspective-view and bird's-eye-view encoding to improve 3D detection and tracking without LiDAR.
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Co-Fusion4D: Spatio-temporal Collaborative Fusion for Robust 3D Object Detection
Co-Fusion4D introduces current-frame-centric spatiotemporal fusion and dual attention to reach 74.9% mAP and 75.6% NDS on nuScenes for 3D detection.