An Multi-Sensors 3D Detection Network Using Guidance-Point-Based Feature Fusion
Chao Wu, Bin Wu · 2022
An accurate and efficient 3D object detection system is crucial to for the autonomous vehicle. However, due to the complexity of the environment, a single sensor, such as LIDAR or camera, cannot meet the safety requirements of autonomous driving. In this paper, a two stage 3D detection network using Guidance-Point-Based feature fusion is proposed. For the first stage network, firstly, the features in the image space are converted to BEV(bird's-eye-view) through the Guidance-Point-Based feature mapping module designed in this paper. Secondly, the LIDAR feature and the camera feature in BEV are fused through the adaptive fusion module, and finally a Cneter-Based strategy is used for detection. In the second stage, keypointed features are used to further refine the objects output by the first stage network. Evaluation on the nuScenes dataset shows that the network we proposed achieves higher accuracy with less additional time.