Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling
This work addresses data scarcity in 3D object detection for autonomous driving, but it is incremental as it builds on existing semi-supervised techniques.
The paper tackles the problem of expensive 3D annotation for outdoor object detection by proposing a semi-supervised framework with adaptive pseudo-labeling, achieving improved performance on the KITTI benchmark.
3D object detection is an important task in computer vision. Most existing methods require a large number of high-quality 3D annotations, which are expensive to collect. Especially for outdoor scenes, the problem becomes more severe due to the sparseness of the point cloud and the complexity of urban scenes. Semi-supervised learning is a promising technique to mitigate the data annotation issue. Inspired by this, we propose a novel semi-supervised framework based on pseudo-labeling for outdoor 3D object detection tasks. We design the Adaptive Class Confidence Selection module (ACCS) to generate high-quality pseudo-labels. Besides, we propose Holistic Point Cloud Augmentation (HPCA) for unlabeled data to improve robustness. Experiments on the KITTI benchmark demonstrate the effectiveness of our method.