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学者姓名：林定
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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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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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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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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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针对基于视觉的增强现实(AR)中虚实注册的准确率和实时效果受光照、遮挡和视角变化影响大,易导致注册失败的问题,提出一种基于二进制鲁棒不变尺度关键点-加速稳健特征(BRISK-SURF)算法的自然特征虚实注册方法.首先,利用加速稳健特征(SURF)特征提取算子检测特征点;然后,采用二进制尺度旋转不变鲁棒(BRISK)特征描述算子对特征点进行二进制描述,结合汉明距离实现准确高速的特征匹配;最后,根据图像间的单应性关系实现虚实注册.从图像特征匹配和虚实注册两方面进行实验,结果显示BRISK-SURF算法的平均准确率与SURF算法基本保持一致,比BRISK算法提高了约25％,平均召回率提高了约10％;基于BRISK-SURF的注册方法的结果接近参考标准数据,精度较高,实时性较好.实验结果表明,所提方法对于光照、遮挡和视角情况不同的图像具有较高的识别准确度、注册精度和实时效果.另外,使用此方法实现了基于AR的交互式旅游资源呈现与体验系统.
Keyword ：
二进制尺度旋转不变鲁棒-加速稳健特征算法 二进制尺度旋转不变鲁棒-加速稳健特征算法 单应性矩阵 单应性矩阵 增强现实 增强现实 自然特征 自然特征 虚实注册 虚实注册
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| GB/T 7714 | 周翔 , 唐丽玉 , 林定 . 基于二进制鲁棒不变尺度关键点-加速稳健特征的自然特征虚实注册方法 [J]. | 计算机应用 , 2020 , 40 (5) : 1403-1408 . |
| MLA | 周翔 et al. "基于二进制鲁棒不变尺度关键点-加速稳健特征的自然特征虚实注册方法" . | 计算机应用 40 . 5 (2020) : 1403-1408 . |
| APA | 周翔 , 唐丽玉 , 林定 . 基于二进制鲁棒不变尺度关键点-加速稳健特征的自然特征虚实注册方法 . | 计算机应用 , 2020 , 40 (5) , 1403-1408 . |
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城市热岛现象随城市化进程而日益突出,种植树木作为缓解热岛的措施越来越受到人们的关注,本文采用ENVI-met软件模拟了不同树高和树宽的种植策略对夏季中午12点时曼切斯特城市某住宅区气温的调节作用,结果表明树高对缓解热岛效果要优于树宽,同等条件下,树冠每增高1米使得研究区域在0.2m和1.4m处的平均温度分别降低0.05K～0.072K和0.04K～0.066K,而树宽每增大1米带来的平均温度的降低不稳定,接近0.015k,因此,建议在城市绿化过程中,同等条件下优先采用具有较高树冠的树木。
Keyword ：
ENVI-met ENVI-met 小气候 小气候 树 树 软件模拟 软件模拟
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| GB/T 7714 | 刘亚敏 , 林定 . 评估曼切斯特居住地绿化树木高宽度对环境温度影响 [C] //2020中国环境科学学会科学技术年会论文集（第四卷） . 2020 . |
| MLA | 刘亚敏 et al. "评估曼切斯特居住地绿化树木高宽度对环境温度影响" 2020中国环境科学学会科学技术年会论文集（第四卷） . (2020) . |
| APA | 刘亚敏 , 林定 . 评估曼切斯特居住地绿化树木高宽度对环境温度影响 2020中国环境科学学会科学技术年会论文集（第四卷） . (2020) . |
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In the space clustering algorithm, because of the choice of the value of k and the problem of the non-clear 'elbow point' of the elbow method, this paper introduces logarithmic function and determines the initial clustering center on the basis of the properties of exponential function, weight adjustment, bigotry term and the basic idea of elbow method, and proposes an improved k-value selection algorithm. Combined with the fully adaptive spectral clustering algorithm, the global terrorist attack data are clustered. It effectively solves the problem that the selection of k value is not clear and the outlier can not be separated in the clustering process. The experimental results show that the clustering method proposed in this paper can not only determine the k value quickly and accurately, but also achieve better clustering effect. CesiumJS, WebVR and speech recognition technology are used to visually display and interact the results. © 2019 Association for Computing Machinery.
Keyword ：
Cluster analysis Cluster analysis Data visualization Data visualization Exponential functions Exponential functions Image processing Image processing K-means clustering K-means clustering Speech recognition Speech recognition Terrorism Terrorism Video signal processing Video signal processing Virtual reality Virtual reality
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| GB/T 7714 | Huang, Guo Xin , Lin, Ding . Clustering analysis and visualization of terrorist attack data [C] . 2019 : 136 . |
| MLA | Huang, Guo Xin et al. "Clustering analysis and visualization of terrorist attack data" . (2019) : 136 . |
| APA | Huang, Guo Xin , Lin, Ding . Clustering analysis and visualization of terrorist attack data . (2019) : 136 . |
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Virtual geographic environments related to dynamic processes contribute to a human understanding of the real world. The results of growth simulations provide good estimations of the future status of forests, but they are typically expressed in plain text summaries, tables or static displays, making it difficult to analyse, understand and further apply the forecast data. The objectives of this study were to propose a strategy for integrating a three-dimensional (3D) geographic environment with growth models and to develop a 3D stand visualization software prototype. Forest growth increments were predicted using the growth models, whereas stand dynamics were simulated using detailed tree models to recognize the changes in the branch whorls and height of individual trees. The spatial structure of the stand was represented by linking each tree diameter class to a spatial distribution according to the features of a Voronoi diagram. The stand visualization system VisForest, which allows users to predict increments in the diameter and height of trees, was extended to estimate the number of trees in each diameter class and to visualize many aspects of a forest stand, e.g., individual tree structure, stem diameter at breast height (DBH, i.e., 1.3m) distribution and height. The software system provides a specialized, intuitive tool for the visualization of a stand, thus facilitating the participation of various stakeholders in management and education.
Keyword ：
Forest Forest Growth simulation Growth simulation Three-dimensional visualization Three-dimensional visualization Virtual geographic environment Virtual geographic environment
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| GB/T 7714 | Tang, Liyu , Peng, Xianmin , Chen, Chongcheng et al. Three-dimensional Forest growth simulation in virtual geographic environments [J]. | EARTH SCIENCE INFORMATICS , 2019 , 12 (1) : 31-41 . |
| MLA | Tang, Liyu et al. "Three-dimensional Forest growth simulation in virtual geographic environments" . | EARTH SCIENCE INFORMATICS 12 . 1 (2019) : 31-41 . |
| APA | Tang, Liyu , Peng, Xianmin , Chen, Chongcheng , Huang, Hongyu , Lin, Ding . Three-dimensional Forest growth simulation in virtual geographic environments . | EARTH SCIENCE INFORMATICS , 2019 , 12 (1) , 31-41 . |
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