Indoor 3D point cloud semantic segmentation is a fundamental task for fine-grained scene understanding and intelligent perception. Due to the prohibitive cost of dense point-wise annotations, weakly supervised learning has emerged as a promising alternative for indoor point cloud segmentation. However, existing weakly supervised methods commonly rely on fixed confidence thresholds for pseudo-label selection, which exhibit limited generalization caused by threshold sensitivity, underutilization of informative low-confidence regions, and progressive noise accumulation during self-training. To address these issues, we propose TGR-T, a weakly supervised framework for indoor 3D point cloud semantic segmentation that incorporates truncated-Gaussian-weighted reliability with adaptive dynamic thresholding. Specifically, a reliability-adaptive dynamic thresholding strategy is introduced to guide pseudo-label selection based on the evolving confidence statistics of unlabeled mini-batches, with exponential moving average smoothing employed to produce stable global estimates and robust separation of reliable and ambiguous regions. To further exploit uncertain regions, a learnable truncated Gaussian weighting function is designed to explicitly model prediction uncertainty within the ambiguous set, providing soft supervision by assigning adaptive weights to low-confidence predictions during optimization. Extensive experimental results demonstrate that the proposed framework significantly enhances the exploitation of unlabeled data under extremely limited supervision: extensive experiments conducted on standard indoor 3D scene benchmarks demonstrate that TGR-T achieves competitive or superior segmentation performance under extremely sparse supervision and can even outperform several fully supervised baselines trained with dense annotations while using only 1% labeled points, thereby substantially narrowing the performance gap between weakly supervised and fully supervised 3D semantic segmentation methods.

This article discusses the event-triggered consensus problem for a switched multi-agent system (MASs) with switching topologies. An observer-based dynamic event-triggered (DET) controller with a discontinuous nonlinear term is designed to reduce arduous communication. With the designed approach, the error system can reach a tracking consensus. Then, a continuous observer-based DET protocol is created using the boundary layer method to prevent chattering effects. Moreover, by employing the Riccati equation and the switched Lyapunov function method, some sufficient criteria are put forward to guarantee the tracking consensus of the systems. The suggested observer-based DET protocol can also exclude the Zeno behavior. Finally, two examples verify the validity of the analysis.

Zero-shot learning enables the recognition of images from unseen classes by leveraging auxiliary semantic information. Traditional methods typically learn either the relationship between the visual features and the semantic vectors or that between the seen and the unseen semantic vectors. However, their zero-shot recognition performances are not ideal for scene images due to large intra-class variations. To address this challenge, we propose a novel approach combining semantic autoencoders (SAEs) and visual relation transfer (VRT), termed SAEVRT. Specifically, we learn two semantic autoencoders for both the seen and the unseen scene classes, which help to alleviate the domain shift between the visual and the semantic spaces. Considering that semantic vectors (no attribute vectors available) are less effective than visual features for scene images, we propose an interpretable seen and unseen visual relation transfer method to learn more effective unseen semantic vectors. By combining SAEs and VRT in a unified learning framework, we exploit both the visual-semantic and seen-unseen relationships. Extensive experiments on four scene datasets demonstrate the superior performance of SAEVRT, achieving recognition accuracies of 63.77%, 67.75%, 58.68%, and 53.26% on Scene15, MIT67, UCM21, and NWPU45, respectively.