SA3Det++: Side-Aware Quality Estimation for Semi-Supervised 3D Object Detection

Abstract

Semi-supervised 3D object detection from point cloud aims to train a detector with a small number of labeled data and a large number of unlabeled data. Among existing methods, the pseudo-label based methods have achieved superior performance, and the core lies in how to select high-quality pseudo-labels with the designed quality evaluation criterion. Despite the success of these methods, they all consider the localization and classification quality estimation from a global perspective. For localization quality, they use a global score threshold to filter out low-quality pseudo-labels and assign equal importance to each side during training, ignoring the fact that sides with different localization quality should not be treat equally. Besides, a large number of pseudo-labels are discarded due to the high global threshold, which may also contain some correctly predicted sides that are helpful for model training. For the classification quality, they usually combine the objectness score and classification confidence score to filter out pseudo-labels. The main focus of them is designing effective classification confidence evaluation metrics, neglecting the importance of predicting better objectness score. In this paper, we propose SA3Det++, a side-aware quality estimation method for semi-supervised object detection, which consists of a probabilistic side localization strategy, a side-aware quality estimation strategy, and a soft pseudo-label selection strategy. Extensive results demonstrate that the proposed method consistently outperforms the baseline methods under different scenes and evaluation criterions.