Autonomous driving paper index
JYOLO: Joint Point Cloud for Autonomous Driving 3D Object Detection
One-line summary
In this paper, a Joint-YOLO fusion model is proposed.
Engineering notes
Key topics: autonomous driving, 3d object detection, object detection, lidar, point cloud. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The camera and lidar are significant sensors for automatic driving, they can provide adequate complementary information. However, 3D point cloud object detection suffers from complexity and low accuracy. In this paper, a Joint-YOLO fusion model is proposed. It provides a low-complexity joint fusion object detection framework. First, the dilated attention is designed to pay attention to the feature resolution of correlation and reduce the number of calculations. And secondly, parallel inverted residual is constructed to connect deep and rich semantic information with high-dimensional features. Finally, the model present an efficient joint fusion structure embedded with camera-lidar detector based 2D-3D bounding box geometric and semantic information for 3D point cloud object detection.
Links and sources
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