Individual tree skeletonization is a fundamental task in forestry remote sensing, which serves as a crucial prerequisite for various downstream applications, ranging from tree structural attribute estimation to carbon cycle modeling. Nevertheless, most existing skeletonization approaches struggle to generate a compact, centered tree skeleton while preserving detail fidelity and topological rationality. To this end, this paper proposes a water droplet model-driven entropy optimization approach (WDTS) for individual tree skeletonization from Terrestrial Laser Scanning (TLS) point clouds. WDTS models an individual tree TLS point cloud as a system of water droplets with varying masses, by progressively generating the skeleton through simulated droplet contraction, merging, and evaporation processes. Key to our approach is an entropy reduction framework that progressively drives droplets toward compact skeletons. To further enhance the centeredness of the generated tree skeleton, WDTS employs a geometric and topological interwoven optimization strategy, explicitly aligning the skeleton within the center of the branch point clouds by minimizing the sum of the squared residuals. Experiments conducted on three individual tree TLS point cloud datasets with different data acquisition strategies have demonstrated the effectiveness and robustness of the proposed WDTS. Compared with previous methods, especially the state-of-the-art Dijkstra-enhanced L1-medial method, WDTS remarkably improves the compactness and centeredness of the skeletons with well-preserved local branch details, reducing the averaged (Formula presented) by (Formula presented), (Formula presented), and (Formula presented) on the single-scan, multi-scan, and simulated dataset, respectively. The generated tree skeletons, including not only the tree skeleton points but also topologically coherent edges, provide a robust foundation for downstream tasks, including precise tree geometry modeling, biomass estimation, and forestry-related sustainable development applications. The code of the proposed WDTS is available at https://github.com/Putaonjfu/WDTS.

Highlights: What are the main findings? We propose a dual-constraint edge-collapse simplification method, jointly enforcing structural constraints and semantic constraints to generate lightweight 3D building models while maintaining high geometric accuracy and semantic consistency. Experiments on Sketchfab, ArCH, STPLS3D and SUM datasets demonstrate the superiority of the proposed method in preserving key structural features of building models under high compression ratios compared to existing methods. What are the implications of the main findings? We provide an effective approach for building model simplification, which is particularly useful for mitigating fine-scale structural degradation and semantic discontinuities commonly observed in conventional methods. The generated lightweight building models are enriched with both geometric details and semantic context, supporting CityGML-compliant 3D model storage, intelligent management, and downstream analysis in digital twin applications. Achieving lightweight representations of building mesh models with accurate geometry and fine structural details is a key challenge in urban 3D modelling. Most existing mesh simplification methods focus on minimizing geometric error while neglecting the specific characteristics of building models in terms of geometric structure and semantic hierarchy, thus leading to structural degradation and semantic inconsistencies. To address this issue, this paper proposes a structure–semantic dual-constrained edge-collapse decimation method for simplifying dense building mesh models reconstructed from point clouds. Our core innovation lies in the joint enforcement of geometric structural constraints and building semantic constraints to effectively preserve both geometric structural features and component-level semantic structures of the models. By incorporating these two constraints, we adaptively assign higher collapse penalties to key structural edges and semantic boundaries, achieving lightweight building model simplification while maintaining fine-level structural details even under high compression ratios. Our method is extensively validated on several datasets of varying scales and complexities, including single-building models from Sketchfab, the large-scale urban datasets SUM and STPLS3D, and the ArCH cultural heritage dataset. Experimental results demonstrate that our method achieves superior or comparable performance compared to the existing methods across all the test datasets, consistently achieving lower or on-par geometric errors measured by RMSE and MAE. Furthermore, our simplified results can be semantically organized and stored under the CityGML paradigm, which provides a unified data support for sharing, semantic retrieval, downstream analysis, and other applications of lightweight building models.

Accurate segmentation and analysis of individual trees from 3D point clouds is a crucial yet challenging task in urbanism and environmental studies. Most existing methods for tree instance segmentation suffer from either under- or over-segmentation errors, mainly due to the complex nature of the environments and the varying tree geometries. In this paper, we propose SATree, a novel structure-aware approach that directly identifies important tree structures, such as crowns and stems, from point clouds, enabling robust tree instance segmentation against tree overlaps and varying tree sizes. Our method leverages a multi-task learning framework that simultaneously performs (i) semantic segmentation to classify a point as crown, stem, or other; (ii) heatmap prediction to assign a heat value to each point based on 2D Gaussian kernels centered at tree stem locations; (iii) offset prediction to estimate point-wise offset vectors pointing to the instance centroid. Key to our approach is the stem localization module, where we fuse the semantic and heatmap predictions to reliably localize tree stems from the network outputs. After that, we utilize a graph-based shortest path algorithm to group individual tree points by integrating the learned offset embeddings. Extensive experiments on two public forestry datasets, TreeML and ForInstance, demonstrate that SATree consistently outperforms state-of-the-art methods in terms of AP, AP50, and AP25 scores, reducing significant under- or over-segmentation errors. Our research output supports downstream forestry inventory, 3D tree reconstruction, and fine-grained part segmentation of trees. Our source code of SATree is available at https://github.com/shenglandu/SATree.

Feedforward fully convolutional neural networks currently dominate in semantic segmentation of 3D point clouds. Despite their great success, they suffer from the loss of local information at low-level layers, posing significant challenges to accurate scene segmentation and precise object boundary delineation. Prior works either address this issue by post-processing or jointly learn object boundaries to implicitly improve feature encoding of the networks. These approaches often require additional modules which are difficult to integrate into the original architecture. To improve the segmentation near object boundaries, we propose a boundary-aware feature propagation mechanism. This mechanism is achieved by exploiting a multitask learning framework that aims to explicitly guide the boundaries to their original locations. With one shared encoder, our network outputs (i) boundary localization, (ii) prediction of directions pointing to the object's interior, and (iii) semantic segmentation, in three parallel streams. The predicted boundaries and directions are fused to propagate the learned features to refine the segmentation. We conduct extensive experiments on the S3DIS and SensatUrban datasets against various baseline methods, demonstrating that our proposed approach yields consistent improvements by reducing boundary errors. Our code is available at https://github.com/shenglandu/PushBoundary.