A weakly supervised method for 3D object detection with partially annotated samples

In numerous practical applications, particularly in the field of autonomous driving, acquiring annotated datasets that include both images and LiDAR point clouds simultaneously presents significant challenges and incurs substantial costs. To overcome the limitations of limited sample annotations, we propose an innovative weakly supervised learning methodology that utilizes reciprocal knowledge transfer between image detection models and 3D point cloud detection models. To the best of our knowledge, this area has not been explored by prior research teams. Our approach effectively addresses the alignment challenge of diverse modal features from an aerial perspective. Through heatmap prediction, we successfully facilitate knowledge transfer between the image detection and 3D point cloud detection models. Additionally, we conduct extensive experiments to evaluate the performance of our models under different parameters in the domain adaptation process, employing Exponential Moving Average (EMA) progressive learning. Furthermore, we explore the advantages of incorporating regression and prediction fusion heads to enhance weakly supervised learning. Remarkably, our experimental results on the widely accessible KITTI datasets demonstrate that our proposed approach achieves outstanding performance in 3D object detection under weak supervision, surpassing the baseline performance of the original 3D point cloud detection model.