🤖 AI Summary
This work addresses the limited generalization capability in high-definition map construction caused by scarce annotated data by proposing a semi-supervised learning approach based on a teacher–student framework. A teacher model trained on a small set of labeled data generates fine-grained pseudo-labels by modeling temporal observation confidence via a Beta distribution and preserving high-confidence regions through a spatial cropping mechanism. These refined pseudo-labels, combined with an optimized map prior, guide the training of the student model. Unlike conventional coarse-grained strategies that discard entire elements, the proposed method retains informative regions, significantly improving performance under low-label regimes. Evaluated on the nuScenes dataset, the approach achieves a 6.1 mAP gain using only minimal labeled data, effectively alleviating dependence on extensive annotations.
📝 Abstract
A critical challenge in deploying online HD map construction systems to real-world scenarios is the scarcity of labeled training data, which limits model generalization in diverse environments. To address this limitation, we propose a teacher-student semi-supervised learning (SSL) framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement. Our approach first trains a teacher model on limited labeled data, then leverages Beta-distribution-based confidence maps to assess the reliability of predicted map elements across temporal observations. Unlike conventional filtering methods that discard entire elements, we introduce a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments. The refined map elements serve as map priors that improve the teacher model's prediction accuracy on unlabeled data in a second pass. These enhanced predictions become pseudo-labels for training a student model from scratch, followed by fine-tuning on the original labeled data. Experimental results on the nuScenes dataset demonstrate that our teacher-student framework with refined pseudo-labels improves performance by +6.1 mAP under a low-label regime compared to training on labeled data alone, offering a practical solution to the labeled data scarcity problem in online HD map construction.