Semi Supervised 3D Object Detection

Semi-supervised 3D object detection aims to train accurate 3D object detectors using limited labeled data and abundant unlabeled data, reducing the high cost of annotation. Current research focuses on improving the quality of pseudo-labels generated for unlabeled data through techniques like teacher-student frameworks, data augmentation strategies (including novel approaches like object-level and channel augmentations), and refined target assignment methods. These advancements are significant because they enable the development of robust 3D object detectors for applications like autonomous driving and robotics, where large labeled datasets are often impractical to obtain.