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首页> 外文会议>2010 IEEE/ACS International Conference on Computer Systems and Applications >3D building modeling based on 3D line grouping using centroid neural network
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3D building modeling based on 3D line grouping using centroid neural network

机译：基于质心神经网络的基于3D线分组的3D建筑物建模

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摘要

Building reconstruction from aerial image data has been studied in this paper. 3D line segments generated by using stereo image analysis are usually fragmented, and it is very hard to reconstruct building rooftop from segmented 3D lines. Centroid neural network algorithm is employed to classify 3D lines into groups of lines. With this grouping technology, the grouped 3D lines are easily clustered into rooftop, and the 3D building model is reconstructed. The proposed approach is evaluated on the Avenches dataset of Ascona aerial images. This experimental results indicate that the grouped 3D lines can be efficiently used for the construction of 3D site models, and prove the efficiency of the proposed approach in dealing with the building reconstruction problem from complicated images.
机译：本文研究了从航空影像数据中重建建筑物的方法。通常，使用立体图像分析生成的3D线段会被分割，因此很难从分割的3D线重建建筑物屋顶。采用质心神经网络算法将3D线分类为线组。借助此分组技术，可以轻松将分组的3D线聚集成屋顶，并重建3D建筑模型。该方法在Ascona航空影像的Avenches数据集上进行了评估。该实验结果表明，分组的3D线可以有效地用于3D站点模型的构建，并从复杂的图像证明了该方法在处理建筑物重建问题方面的效率。

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