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The mean is a measure of the central tendency of a data set. In some data sets, the data is inherently multiplicative, and the arithmetic mean is not useful. For example, the human population multiplies with time, and so does the credit amount of financial investment, as the interest compounds over successive time intervals.
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In systems where values diminish by a constant proportion at each stage, the resulting sequence follows a geometric structure. Each new value in the sequence is obtained by applying a fixed multiplier to the preceding term. This regular, proportional decline type is often used to represent processes involving gradual loss, such as energy dissipation or reduction in amplitude over time.When analyzing the total effect of such a process across unlimited iterations, the series of values is referred...
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If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
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Human behavior is intricately shaped by social influences that arise from interactions with others in diverse contexts. These influences not only mold beliefs and attitudes but also drive the regulation of behaviors through both direct communication and observational learning. The study of these processes falls within the domain of social psychology, which seeks to understand how individuals are affected by and affect those around them.Mechanisms of Social InfluenceDirect social influence...
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Movement joints in buildings are essential design elements that accommodate inevitable motions caused by various factors such as temperature changes, moisture content variations, and structural deflections. These motions, if not considered in design and construction, can lead to unsightly or dangerous damage. Movement joints are incorporated in different forms to manage these stresses and allow materials to move without causing distress.
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一种用于3D建筑个性化的方法,集成SAMPolyBuild和多个空间几何特征.

Lianshuai Cao1, Yi Cheng1, Zheng Zhang1

  • 1Institute of Geographic Spatial Information, PLA Information Engineering University, Zhengzhou 450001, China.

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概括

本研究引入了一种新的方法,用于使用SAMPolyBuild和空间特征在3D模型中分离单个建筑物. 该方法提高了3D建筑个性化的准确性,为手工方法提供了更有效的替代方案.

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3D建筑个性化 3D建筑个性化地球移动器的距离詹森-香农分歧是什么意思在SAMPolyBuild中,使用的是SAMPolyBuild空间 - 几何特征的空间特征.

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科学领域:

  • 地理信息学是一种地理信息学.
  • 计算机视觉 计算机视觉
  • 3D 建模 3D 建模

背景情况:

  • 准确的3D建筑模型对于城市规划和管理至关重要.
  • 现有的3D建筑个性化的方法通常是手动和低效的,或者依赖于深度学习,具有广泛的预训练和对空间结构的敏感性.

研究的目的:

  • 为3D建筑个性化提出一种新,高效和准确的方法.
  • 为了克服手动分离和深度学习方法在3D建筑模型建立中的局限性.

主要方法:

  • 整合SAMPolyBuild (多边形的分段任何模型) 与多个空间几何特征,用于3D建筑个性化.
  • 利用SAMPolyBuild的零射击学习进行粗的建筑提取,然后使用空间几何特征进行改进.
  • 介绍Jensen-Shannon分歧和地球移动器距离作为增强建筑物识别的统计参数.

主要成果:

  • 拟议的方法有效地从城市斜光谱3D模型中提取单个建筑模型.
  • 在Semantic Urban Meshes (SUM) 数据集上,对于具有典型空间结构的建筑物,获得了大约0.83的F1得分.
  • 与现有方法相比,在准确性和效率方面表现出卓越的性能.

结论:

  • 这种新的方法为3D建筑个性化提供了可行和有效的解决方案.
  • 整合SAMPolyBuild和空间几何特征,包括新的统计参数,显著提高了3D建筑提取的准确性.
  • 这种方法为详细的3D城市模型的自动生成提供了有希望的进步.