3DResT: A Strong Baseline for Semi-Supervised 3D Referring Expression Segmentation

3D Referring Expression Segmentation (3D-RES) typically requires extensive instance-level annotations for fully supervised learning, a process that is both time-consuming and costly. Semi-supervised learning (SSL) can address this by using a small amount of labeled data and a large amount of unlabeled data, improving performance while reducing the annotation costs. SSL adopts a teacher-student learning paradigm, where the teacher produces pseudo-labels to guide the student, often using high-confidence threshold filtering to improve pseudo-label quality. However, in the context of 3D-RES, where each label corresponds to a single mask and labeled data is scarce, existing SSL methods treat high-quality pseudo-labels merely as auxiliary supervision, which hinders their ability to fully boost the model's learning potential. The reliance on high-confidence thresholds for filtering often results in potentially valuable pseudo-labels being discarded, restricting the model's ability to leverage the abundant unlabeled data. Therefore, we identify two critical challenges in semi-supervised 3D-RES, namely, inefficient utilization of high-quality pseudo-labels and wastage of useful information from low-quality pseudo-labels. In this paper, we introduce the first semi-supervised learning framework for 3D-RES, presenting a robust baseline method named 3DResT. To address these challenges, we propose two novel designs called Teacher-Student Consistency-Based Sampling (TSCS) and Quality-Driven Dynamic Weighting (QDW). TSCS aids in the selection of high-quality pseudo-labels, integrating them into the labeled dataset to strengthen the labeled supervision signals. QDW preserves low-quality pseudo-labels by dynamically assigning them lower weights, allowing for the effective extraction of useful information rather than discarding them. Extensive experiments conducted on the widely used benchmark demonstrate the effectiveness of our method. Notably, with only 1% labeled data, 3DResT achieves an mIoU improvement of 8.34 points compared to the fully supervised method.