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学者姓名：林定
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Aiming at the segmentation and extraction of the main part of substation equipment, we use Fast Point Feature Histograms (FPFH) and Locally Convex Connected Patches (LCCP) to obtain voxels’ integrated geometric features, then aggregate these features and their K nearest neighbors’ on voxels to build multi-level voxels’ features by bottom-up hierarchy, and achieve pre-segmentation of shapes with the flow-constrained super-voxel clustering algorithm; After the pre-segmentation, we conduct shape analysis to extract semantically meaningful instances of equipment components, achieving part-level point cloud data instance extraction of artificial equipment geometric features. Without training data or manual annotations, the work presented is simple and easy to implement. It can merge patches across surface-singularities. It needs a few parameters, can achieve automatic 3D instance extraction from point clouds for different scenes with the same or similar parameters. © 2025 SPIE.
Keyword ：
Fiber optic sensors Fiber optic sensors Image coding Image coding Nearest neighbor search Nearest neighbor search Network security Network security Prisms Prisms Semantic Segmentation Semantic Segmentation Superpixels Superpixels
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| GB/T 7714 | Zhang, Meng , Lin, Ding , Ding, Jing et al. Unsupervised part extraction of substation equipment based on joint multi-level voxels’ features of point clouds [C] . 2025 . |
| MLA | Zhang, Meng et al. "Unsupervised part extraction of substation equipment based on joint multi-level voxels’ features of point clouds" . (2025) . |
| APA | Zhang, Meng , Lin, Ding , Ding, Jing , Fang, Tao . Unsupervised part extraction of substation equipment based on joint multi-level voxels’ features of point clouds . (2025) . |
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In recent years, large-scale point cloud semantic segmentation has been widely applied in various fields, such as remote sensing and autonomous driving. Most existing point cloud networks use local aggregation to abstract unordered point clouds layer by layer. Among these, position embedding serves as a crucial step. However, current methods of position embedding have limitations in modeling spatial relationships, especially in deeper encoders where richer spatial positional relationships are needed. To address these issues, this paper summarizes the advantages and disadvantages of mainstream position embedding methods and proposes a novel Hybrid Offset Position Encoding (HOPE) module. This module comprises two branches that compute relative positional encoding (RPE) and offset positional encoding (OPE). RPE combines explicit encoding to enhance position features through attention, learning position bias implicitly, while OPE calculates absolute position offset encoding by considering differences with grouping embeddings. These two encodings are adaptively mixed in the final output. The experiment conducted on multiple datasets demonstrates that our module helps the deep encoders of the network capture more robust features, thereby improving model performance on various baseline models. For instance, PointNet++ and PointMetaBase enhanced with HOPE achieved mIoU gains of 2.1% and 1.3% on the large-scale indoor dataset S3DIS area-5, 2.5% and 1.1% on S3DIS 6-fold, and 1.5% and 0.6% on ScanNet, respectively. RandLA-Net with HOPE achieved a 1.4% improvement on the large-scale outdoor dataset Toronto3D, all with minimal additional computational cost. PointNet++ and PointMetaBase had approximately only a 0.1 M parameter increase. This module can serve as an alternative for position embedding, and is suitable for point-based networks requiring local aggregation.
Keyword ：
attention mechanism attention mechanism large-scale point cloud large-scale point cloud local aggregation local aggregation positional encoding positional encoding position embedding position embedding semantic segmentation semantic segmentation
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| GB/T 7714 | Xiao, Yu , Wu, Hui , Chen, Yisheng et al. Hybrid Offset Position Encoding for Large-Scale Point Cloud Semantic Segmentation [J]. | REMOTE SENSING , 2025 , 17 (2) . |
| MLA | Xiao, Yu et al. "Hybrid Offset Position Encoding for Large-Scale Point Cloud Semantic Segmentation" . | REMOTE SENSING 17 . 2 (2025) . |
| APA | Xiao, Yu , Wu, Hui , Chen, Yisheng , Chen, Chongcheng , Dong, Ruihai , Lin, Ding . Hybrid Offset Position Encoding for Large-Scale Point Cloud Semantic Segmentation . | REMOTE SENSING , 2025 , 17 (2) . |
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Point clouds are essential 3D data representations utilized across various disciplines, often requiring point cloud completion methods to address inherent incompleteness. Existing completion methods like SnowflakeNet only consider local attention, lacking global information of the complete shape, and tend to suffer from overfitting as the model depth increases. To address these issues, we introduced self-positioning point-based attention to better capture complete global contextual features and designed a Channel Attention module for adaptive feature adjustment within the global vector. Additionally, we implemented a vector attention grouping strategy in both the skip-transformer and self-positioning point-based attention to mitigate overfitting, improving parameter efficiency and generalization. We evaluated our method on the PCN dataset as well as the ShapeNet55/34 datasets. The experimental results show that our method achieved an average CD-L1 of 7.09 and average CD-L2 scores of 8.0, 7.8, and 14.4 on the PCN, ShapeNet55, ShapeNet34, and ShapeNet-unseen21 benchmarks, respectively. Compared to SnowflakeNet, we improved the average CD by 1.6%, 3.6%, 3.7%, and 4.6% on the corresponding benchmarks, while also reducing complexity and computational costs and accelerating training and inference speeds. Compared to other existing point cloud completion networks, our method also achieves competitive results.
Keyword ：
3D point cloud 3D point cloud attention mechanism attention mechanism deep learning deep learning point cloud completion point cloud completion
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| GB/T 7714 | Xiao, Yu , Chen, Yisheng , Chen, Chongcheng et al. GSSnowflake: Point Cloud Completion by Snowflake with Grouped Vector and Self-Positioning Point Attention [J]. | REMOTE SENSING , 2024 , 16 (17) . |
| MLA | Xiao, Yu et al. "GSSnowflake: Point Cloud Completion by Snowflake with Grouped Vector and Self-Positioning Point Attention" . | REMOTE SENSING 16 . 17 (2024) . |
| APA | Xiao, Yu , Chen, Yisheng , Chen, Chongcheng , Lin, Ding . GSSnowflake: Point Cloud Completion by Snowflake with Grouped Vector and Self-Positioning Point Attention . | REMOTE SENSING , 2024 , 16 (17) . |
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Indoor point clouds often present significant challenges due to the complexity and variety of structures and high object similarity. The local geometric structure helps the model learn the shape features of objects at the detail level, while the global context provides overall scene semantics and spatial relationship information between objects. To address these challenges, we propose a novel network architecture, PointMSGT, which includes a multi-scale geometric feature extraction (MSGFE) module and a global Transformer (GT) module. The MSGFE module consists of a geometric feature extraction (GFE) module and a multi-scale attention (MSA) module. The GFE module reconstructs triangles through each point's two neighbors and extracts detailed local geometric relationships by the triangle's centroid, normal vector, and plane constant. The MSA module extracts features through multi-scale convolutions and adaptively aggregates features, focusing on both local geometric details and global semantic information at different scale levels, enhancing the understanding of complex scenes. The global Transformer employs a self-attention mechanism to capture long-range dependencies across the entire point cloud. The proposed method demonstrates competitive performance in real-world indoor scenarios, with a mIoU of 68.6% in semantic segmentation on S3DIS and OA of 86.4% in classification on ScanObjectNN.
Keyword ：
geometric feature geometric feature multi-scale attention multi-scale attention point cloud analysis point cloud analysis real-world indoor scenario real-world indoor scenario transformer transformer
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| GB/T 7714 | Chen, Yisheng , Xiao, Yu , Wu, Hui et al. Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis [J]. | MATHEMATICS , 2024 , 12 (23) . |
| MLA | Chen, Yisheng et al. "Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis" . | MATHEMATICS 12 . 23 (2024) . |
| APA | Chen, Yisheng , Xiao, Yu , Wu, Hui , Chen, Chongcheng , Lin, Ding . Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis . | MATHEMATICS , 2024 , 12 (23) . |
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This study aims to address the challenge of lightweighting point cloud data at the part level in substation equipment, aligning with the requirements of digital twin applications for grid substations and considering the hierarchical structure of equipment. Utilizing the importance of equipment parts, we construct a priority queue and integrate the Draco algorithm to automatically lightweight transformer equipment point cloud into semantic models with multiple levels of detail at the part level. This approach effectively resolves the lightweighting issue of point cloud data for substation equipment at the part level, achieving a differential treatment where key parts maintain high original data retention rates while minor parts occupy fewer resources. In comparison to applying the global Draco algorithm to equipment, our method offers the advantage of flexibly allocating storage and transmission resources to user-focused key targets while keeping the overall compression rate unchanged.
Keyword ：
Draco algorithm Draco algorithm hierarchical structure hierarchical structure lightweight lightweight substations equipment substations equipment
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| GB/T 7714 | Zhang, Meng , Lin, Ding , Ding, Jing et al. Differential Lightweight Processing in Digital Twin construction of substations [J]. | 2024 3RD INTERNATIONAL CONFERENCE ON ENERGY AND ELECTRICAL POWER SYSTEMS, ICEEPS 2024 , 2024 : 354-360 . |
| MLA | Zhang, Meng et al. "Differential Lightweight Processing in Digital Twin construction of substations" . | 2024 3RD INTERNATIONAL CONFERENCE ON ENERGY AND ELECTRICAL POWER SYSTEMS, ICEEPS 2024 (2024) : 354-360 . |
| APA | Zhang, Meng , Lin, Ding , Ding, Jing , Fang, Tao . Differential Lightweight Processing in Digital Twin construction of substations . | 2024 3RD INTERNATIONAL CONFERENCE ON ENERGY AND ELECTRICAL POWER SYSTEMS, ICEEPS 2024 , 2024 , 354-360 . |
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针对冠层大小的差异及在多大程度上影响不同深浅街谷内部行人小气候的问题,构建NE-WS和N-S走向的浅、中、深 3 种纵横比特征的街道,并利用ENVI-met模拟软件,分析不同大小的树冠对行人气温影响.结果表明:1)无论街谷深浅如何,植树总是引起上午行人气温的小幅升高;但是,植树可降低中午和下午的行人气温;2)树木对行人小气候的影响程度随着街谷深度的增加而减弱,因此行道树绿化差异所致的深街谷行人小气候差异将微乎其微;3)行道树对行人小气候的调节作用有限,建议在NE-WS和N-S走向的街道中种植带有小空隙率(0.125)的行道树,而不是让树冠相接形成连续树荫,以获得更好的降温和通风.
Keyword ：
数值模拟 数值模拟 树木绿化 树木绿化 热环境 热环境 街谷 街谷
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| GB/T 7714 | 林定 , 吴俊 , 刘亚敏 et al. 树冠空间占用对不同高宽比街道行人热环境的影响 [J]. | 福州大学学报（自然科学版） , 2023 , 51 (3) : 355-362 . |
| MLA | 林定 et al. "树冠空间占用对不同高宽比街道行人热环境的影响" . | 福州大学学报（自然科学版） 51 . 3 (2023) : 355-362 . |
| APA | 林定 , 吴俊 , 刘亚敏 , 朱牧 . 树冠空间占用对不同高宽比街道行人热环境的影响 . | 福州大学学报（自然科学版） , 2023 , 51 (3) , 355-362 . |
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化学事故具有突发性强、环境污染破坏严重、救援难度大等特点,对突发化学事故的影响范围、破坏程度进行快速评估,为事故抢险救援提供决策支持,是应对重特大突发化学事故、降低事故损失的重要手段.本文基于三维地理信息系统(Three-dimensional Geographic Information System,3D GIS),在充分考虑下垫面影响的前提下,研究了大气污染扩散模型,提出了扩散场快速构建技术及兼顾污染分区的救援力量调度方法,并以此为核心建立了系统总体框架,形成了集扩散模拟、危害分析、救援调度等功能于一体的突发化学事故危害评估与救援辅助决策支持系统,可为突发化学事故危害后果评估与辅助救援决策提供有效的技术支撑.
Keyword ：
3D GIS 3D GIS 动态规划 动态规划 污染扩散模拟 污染扩散模拟 突发化学事故 突发化学事故
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| GB/T 7714 | 朱勇兵 , 林定 , 杨浩锋 et al. 基于3D GIS的突发化学事故危害评估与救援辅助决策支持系统 [J]. | 防化研究 , 2023 , 2 (2) : 58-65 . |
| MLA | 朱勇兵 et al. "基于3D GIS的突发化学事故危害评估与救援辅助决策支持系统" . | 防化研究 2 . 2 (2023) : 58-65 . |
| APA | 朱勇兵 , 林定 , 杨浩锋 , 赵三平 , 吴小竹 , 韩梦薇 et al. 基于3D GIS的突发化学事故危害评估与救援辅助决策支持系统 . | 防化研究 , 2023 , 2 (2) , 58-65 . |
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夏季东西走向的街道因长时间暴露于日照形成最不舒适的行人环境,树木可提供树荫降低气温但也阻碍了局地通风,然而,植树对行人热环境的综合影响尚不清楚.针对城市高密度街区(LCZ 2类型)中东西走向的街道,开展行道树绿化差异对行人热环境综合影响的定量计算与分析.结果表明:1)树木对行人热环境的影响是非线性的,且在夏季白天不同时段呈不同规律,街道树木覆盖率与树致平均行人气温差异值之间存在如下关系:上下午非线性关系强烈,中午则接近线性关系;2)当树木覆盖率小于50％时,树致行人气温降低的梯度大,每增加10％树木覆盖率,行人高度平均气温最高约降低0.26℃;树木覆盖率超过50％以后,植树带来的热效益改善值减小,而且影响的时间窗口趋于中午附近(11:00—14:30);3)东西向街道内的各种植树方案都对行人热环境产生正面影响,当树冠相互接触形成连续树荫时,树致改善效应接近饱和,继续提高种植密度反而会产生负面影响.考虑城市用地紧张,建议东西走向的街道,采用可形成连续树荫的50％左右覆盖率的行道树绿化,以获得舒适的行人小气候.
Keyword ：
天空可视因子 天空可视因子 数值模拟 数值模拟 树木绿化 树木绿化 树木覆盖率 树木覆盖率 行人热环境 行人热环境 风景园林 风景园林
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| GB/T 7714 | 林定 , 吴俊 , 刘亚敏 et al. 福州东西向深街谷内树致行人夏季热环境差异 [J]. | 中国园林 , 2023 , 39 (2) : 120-126 . |
| MLA | 林定 et al. "福州东西向深街谷内树致行人夏季热环境差异" . | 中国园林 39 . 2 (2023) : 120-126 . |
| APA | 林定 , 吴俊 , 刘亚敏 , 邓卓 , 韩朝帅 . 福州东西向深街谷内树致行人夏季热环境差异 . | 中国园林 , 2023 , 39 (2) , 120-126 . |
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This paper selects two streets with different orientations and different aspect ratios as the research area, using ENVI-met software to simulate the effect of trees' canopy occupation on thermal comfort at the pedestrian height. The results show that the trees' canopy occupation ratio (TCR) influences the average air temperature. Tree planting raises air temperature both at noon and afternoon, while dropping it in the morning. For the two streets, when TCR is greater than a certain value, continuing to increase trees' canopy occupation has no significant impact on air temperature. Therefore, larger canopy occupancy of trees does not necessarily cool air temperature. More treeing even worsens the thermal comfort, especially in the streets with a large aspect ratio. © 2022 IEEE.
Keyword ：
Aspect ratio Aspect ratio Atmospheric temperature Atmospheric temperature Employment Employment Reforestation Reforestation Thermal comfort Thermal comfort
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| GB/T 7714 | Ding, Lin , Jun, Wu , Yamin, Liu et al. Influence of trees' canopy occupation on pedestrian thermal environment in summer [C] . 2022 . |
| MLA | Ding, Lin et al. "Influence of trees' canopy occupation on pedestrian thermal environment in summer" . (2022) . |
| APA | Ding, Lin , Jun, Wu , Yamin, Liu , Mu, Zhu , Chongcheng, Chen . Influence of trees' canopy occupation on pedestrian thermal environment in summer . (2022) . |
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Urban ecological landscape has sensory functions, which describe the visual effect of green plants for human beings. Green view index is considered as a relatively good indicator for measuring the visibility of urban green space, which can reflect different levels of urban vegetation space directly. Green view index is usually calculated using static images or street view data. However, green view index is a variable quantity since the variant of view point location results with very different visual effects and the phenological change of plants. What is more, plants, as organism with life characteristics, are the most important elements in the urban green space landscape, which can change their morphology with time factors constantly and affect the amount of visible green space. In the paper, a green view index calculation method was proposed based on the three-dimensional simulation landscape of garden trees driven by spatial information data of geographic entities and tree architecture and growth model. This method comprises three steps. Firstly, using virtual geographical environment, virtual plants, and other technologies, a three-dimensional urban vegetation landscape was generated according to hard landscape data (e.g. roads, building) and tree models. Secondly, based on visual mechanisms of seeing, virtual cameras were constructed to set observation points and generate the landscape visual images. Thirdly, the visibility analysis was conducted to identify vegetation information visible from each observation point at the pixel level, which can compute the value of green view index. A three-dimensional tree landscape simulation and green view index estimation prototype was developed. Taking urban road greenery scenes (e.g. Jinshan avenue in Fuzhou) as an example, the green view index was estimated and analyzed. The results are closed to those derived from street view images. Therefore, it can effectively reflect the visual feeling of vehicle passengers. It is useful for quantitatively evaluating the visual effects of urban forest states of past, present, or future at different growth stages. It is also suitable to simulate and calculate green view index dynamically by setting view points everywhere and in arbitrary directions. The method can be used as a potential tool to assess the simulation results of urban green space design schemes before they were carried out. It is also helpful for the rational planning of urban green space. It can provide references for the science and rationality of the future landscape of different engineering design schemes, thereby promoting the sustainable development of the city. © 2021, Science Press. All right reserved.
Keyword ：
Cameras Cameras Ecology Ecology Forestry Forestry Roads and streets Roads and streets Urban growth Urban growth Vegetation Vegetation Virtual reality Virtual reality Visibility Visibility
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| GB/T 7714 | Jiang, Feng , Tang, Liyu , Lin, Ding et al. Green View Index Estimation Method based on Three-dimensional Simulation of Urban Tree Landscape [J]. | Journal of Geo-Information Science , 2021 , 23 (12) : 2151-2162 . |
| MLA | Jiang, Feng et al. "Green View Index Estimation Method based on Three-dimensional Simulation of Urban Tree Landscape" . | Journal of Geo-Information Science 23 . 12 (2021) : 2151-2162 . |
| APA | Jiang, Feng , Tang, Liyu , Lin, Ding , Chen, Xiaoling , Feng, Xianchao , Chen, Chongcheng . Green View Index Estimation Method based on Three-dimensional Simulation of Urban Tree Landscape . | Journal of Geo-Information Science , 2021 , 23 (12) , 2151-2162 . |
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