3D full-scene segmentation technology has demonstrated great potential driven by large models, but it often faces challenges of incomplete scenes and identification of invisible classes in practical applications. To address this, we propose the LR-AdaInSeg method, which significantly enhances the model’s generalization ability in incomplete scenes through two key innovations: First, we design a Bayesian Low-Rank Module, which effectively solves the problem of feature space redundancy through dynamic optimization of the network structure, improving adaptability to incomplete scenes. Second, we combine graph contrastive clustering with the Low-Rank module, leveraging its robust feature representation capability to achieve accurate differentiation of invisible classes. In terms of implementation, we build a multi-scale feature extraction framework based on the 3D U-Net and utilize the 3D prompt points and their 2D masks as supervisory signals to achieve effective fusion of geometric and semantic information. Experiments show that our method achieves advanced performance on multiple benchmarks such as ScanNet, particularly excelling in handling incomplete scenes and invisible class objects.