3D object detection based on local feature fusion

The existing 3D object detection networks based on the cross-attention mechanism from the bird's-eye view perspective mostly use the parameters calibrated by the camera to take the features corresponding to project each 3D feature point from the front view feature by projecting the 3D point onto the two-dimensional image. The features obtained by this method are affected by the calibration parameters and the projection position. To reduce the influence of the camera calibration parameters and the accuracy of the projection position on the obtained features, a local feature fusion network is proposed, which fuses the features within a certain range near the projection point to represent the corresponding features of the 3D point, thus improving the feature representation capability. The experimental results on the nuScenes val set show that the NDS value is increased by 1.4% compared with the benchmark. In addition, the network has robustness. Under the same noise, its accuracy reduction rate is reduced by more than 56% compared with the benchmark and has a practical value in automatic driving.