Semantic segmentation of large-scale point clouds provides foundational knowledge for various geodetic and cartographic applications, including autonomous driving, smart cities, and indoor navigation. However, point cloud data’s unstructured and inherently disordered characteristics pose challenges in extracting accurate 3-D semantic information. In this study, we introduce a novel semantic segmentation network for large-scale point cloud scenes, referred to as dense dual-branch cross attention network (D2CAN). We propose a local multidimensional feature aggregation (LMFA) module to increase multidimensional feature representation types and preserve rich local details. Based on the augmented local features, an expanded dual-branch cross attention (EDCA) module establishes internal deep connections between multidimensional attributes and semantic features. This assists the network in reducing boundary ambiguities and expanding the receptive field, enabling the parallel capture of long-range contexts specifically adapted for large-scale scene point cloud segmentation. These two modules work collaboratively to constitute a local context deep perception (LCDP) block. To reduce information loss during feature sampling and propagation, we propose a global feature pyramid dense fusion (GFDF) block. This block adaptively integrates features across different scales and effectively captures global context with long-range dependencies. In conclusion, D2CAN combines LCDP and GFDF to aggregate both local and global contexts, resulting in robust feature discrimination for semantic segmentation of large-scale scenes. Our method’s effectiveness and superior generation ability have been validated across three challenging benchmarks and achieve state-of-the-art performance on Toronto-3D, SensatUrban, and Stanford large-scale 3-D indoor spaces (S3DIS) datasets, with mean intersection over union (IoU) values of 83.5%, 61.1%, and 72.3%, respectively.

Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constraints, including extracted structural planes, local surface convexity, and color information of objects for indoor segmentation. Firstly, the raw point cloud is partitioned into surface patches, and we propose a robust plane extraction method to extract the main structural planes of the indoor scene. Then, the match between the surface patches and the structural planes is achieved by global energy optimization. Next, we closely integrate multiple constraints mentioned above to design a graph clustering algorithm to partition cluttered indoor scenes into object parts. Finally, we present a post-refinement step to filter outliers. We conducted experiments on a benchmark RGB-D dataset and a real indoor laser-scanned dataset to perform numerous qualitative and quantitative evaluation experiments, the results of which have verified the effectiveness of the MCGC method. Compared with state-of-the-art methods, MCGC can deal with the segmentation of indoor scenes more efficiently and restore more details of indoor structures. The segment precision and the segment recall of experimental results reach 70% on average. In addition, a great advantage of the MCGC method is that the speed of processing point clouds is very fast; it takes about 1.38 s to segment scene data of 1 million points. It significantly reduces the computation overhead of scene point cloud data and achieves real-time scene segmentation.

Urban airborne laser scanning (ALS) point clouds cover extensive geographical areas, rendering dense point-level annotation economically prohibitive and limiting the feasibility of fully supervised learning. In weakly supervised settings for urban ALS data, the natural long-tailed class distribution—where ground and building points dominate and smaller objects are rare—combined with the use of fixed pseudo-label thresholds under sparse annotations exacerbates confirmation bias and increases prediction uncertainty. This ultimately restricts the effective utilization of unlabeled data during training. To overcome these challenges, we propose a pseudo-label confidence-calibrated curriculum learning framework designed for weakly supervised ALS point cloud classification. The framework introduces a confidence-aware self-adaptive soft gating (CSS) mechanism that dynamically adjusts category-specific thresholds online using exponential moving average statistics and scene-aware normalization, eliminating the need for manual scheduling while improving pseudo-label quality. In addition, a reliability-driven soft selection (RSS) constraint is incorporated, in which each point is assigned a comprehensive reliability score that integrates prediction confidence, entropy clarity, and cross-augmentation consistency, enabling adaptive soft weighting to replace hard pseudo-label selection and achieve more balanced sample utilization. These components are further integrated into a unified pseudo-label confidence-calibrated curriculum learning framework (P3CL) that progressively shifts the model’s focus from high-certainty samples to more ambiguous ones, effectively mitigating confirmation bias. Extensive experiments on three public ALS benchmarks demonstrate that the proposed method consistently outperforms existing weakly supervised approaches and achieves competitive performance compared with several fully supervised models.

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.

Airborne Laser Scanning (ALS) point clouds, characterized by low point density due to extensive ground coverage, pose significant challenges in labeling work. Additionally, the ALS scanning pattern can lead to occlusions, resulting in data gaps and further increasing the difficulty of accurately labeling points within obstructed areas. In this context, fully supervised deep learning methods, which rely on a large volume of precisely annotated point-level data, are not well-suited due to the time-consuming and costly nature of obtaining extensive labeled datasets. Addressing these challenges, we propose an innovative weakly supervised learning strategy: a dense supervised dual-aware contrastive learning approach (DSDCL). We introduce a context-aware contrastive constraint to enhance the model’s ability to differentiate similar data points by leveraging subtle contextual differences, improving feature learning robustness in environments with incomplete objects. Additionally, we apply a scene-aware dense contrastive constraint to enable multi-scale point cloud representations while enforcing class-aware feature space constraints through gradient-based learning across multiple layers. We employ this contrastive constraint to enhance category discrepancies in prototype features predicted by pseudo-labels from dual-branch data, improving model robustness to categorical distribution differences by focusing on class prototype features and mitigating the impact of erroneous pseudo-labels. Extensive experiments on three benchmark ALS datasets demonstrate that DSDCL achieves performance comparable to advanced fully supervised methods, even when using only 0.1% of labeled samples.