A Dual RoI Feature Fusion for 3D Object Detection

Following the continuous improvement of 3D sensing technologies, Three-dimensional object detection is extensively utilized in autonomous driving scenarios. Current methods still face challenges in terms of high-precision detection. Therefore, we propose a voxel-based 3D object detection algorithm. Traditional voxelization results in the loss of critical information, so we introduce dynamic attention voxel feature encoding to preserve complete spatial information and focus on foreground features. We propose drop sparse convolution to suppress submanifold dilation in 3D feature extraction. To refine object localization and classification tasks, we also design an auxiliary RoI pooling method to extract 2D RoI features from obtained BEV map and integrate them with 3D RoI features. The quantitative evaluation on the KITTI dataset indicates that our method outperforms several advanced methods in terms of vehicle detection accuracy and also demonstrates good performance on other performance metrics. In addition, the average accuracy has been significantly improved compared to the baseline.