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Found 3 papers, 3 papers with code
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras
Three-dimensional perception from multi-view cameras is a crucial component in autonomous driving systems, which involves multiple tasks like 3D object detection and bird's-eye-view (BEV) semantic segmentation.
RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection
In the dual-stream radar backbone, a point-based encoder and a transformer-based encoder are proposed to extract radar features, with an injection and extraction module to facilitate communication between the two encoders.
Ranked #2 on
3D Object Detection
on nuscenes Camera-Radar
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios
Based on the property of outdoor point clouds in autonomous driving scenarios, i. e., the point clouds of distant objects are more sparse, we propose point density prediction to enable the 3D encoder to learn location information, which is essential for object detection.
