🤖 AI Summary
Existing semi-supervised 3D object detection methods suffer from low-quality pseudo-labels, manually tuned confidence thresholds, and insufficient exploitation of contextual information. Method: We propose an adaptive pseudo-label selection framework within a teacher–student paradigm. It introduces a learnable, context-aware thresholding module that dynamically generates class- and distance-dependent confidence thresholds based on object proximity, category, and model learning status. A soft supervision strategy is incorporated to mitigate noise from erroneous pseudo-labels. Furthermore, dual-network collaboration enables score fusion and quality assessment, with spatial alignment between pseudo-labels and ground-truth bounding boxes serving as the supervision signal. Results: Evaluated on KITTI and Waymo Open Dataset, our method achieves significant improvements in detection accuracy and recall, particularly for hard examples, and consistently outperforms state-of-the-art semi-supervised 3D detectors.
📝 Abstract
Semi-supervised 3D object detection (SS3DOD) aims to reduce costly 3D annotations utilizing unlabeled data. Recent studies adopt pseudo-label-based teacher-student frameworks and demonstrate impressive performance. The main challenge of these frameworks is in selecting high-quality pseudo-labels from the teacher's predictions. Most previous methods, however, select pseudo-labels by comparing confidence scores over thresholds manually set. The latest works tackle the challenge either by dynamic thresholding or refining the quality of pseudo-labels. Such methods still overlook contextual information e.g. object distances, classes, and learning states, and inadequately assess the pseudo-label quality using partial information available from the networks. In this work, we propose a novel SS3DOD framework featuring a learnable pseudo-labeling module designed to automatically and adaptively select high-quality pseudo-labels. Our approach introduces two networks at the teacher output level. These networks reliably assess the quality of pseudo-labels by the score fusion and determine context-adaptive thresholds, which are supervised by the alignment of pseudo-labels over GT bounding boxes. Additionally, we introduce a soft supervision strategy that can learn robustly under pseudo-label noises. This helps the student network prioritize cleaner labels over noisy ones in semi-supervised learning. Extensive experiments on the KITTI and Waymo datasets demonstrate the effectiveness of our method. The proposed method selects high-precision pseudo-labels while maintaining a wider coverage of contexts and a higher recall rate, significantly improving relevant SS3DOD methods.