کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4952554 | 1442471 | 2017 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Urban building reconstruction from raw LiDAR point data
ترجمه فارسی عنوان
بازسازی ساختمان شهری از داده های نقطه خام لی دار
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
گرافیک کامپیوتری و طراحی به کمک کامپیوتر
چکیده انگلیسی
We present a method for automatic reconstruction of the volumetric structures of urban buildings, directly from raw LiDAR point clouds. Given the large-scale LiDAR data from a group of urban buildings, we take advantage of the “divide-and-conquer” strategy to decompose the entire point clouds into a number of subsets, each of which corresponds to an individual building. For each urban building, we determine its upward direction and partition the corresponding point data into a series of consecutive blocks, achieved by investigating the distributions of feature points of the building along the upward direction. Next, we propose a novel algorithm, Spectral Residual Clustering (SRC), to extract the primitive elements within the contours of blocks from the sectional point set, which is formed by registering the series of consecutive slicing points. Subsequently, we detect the geometric constraints among primitive elements through individual fitting, and perform constrained fitting over all primitive elements to obtain the accurate contour. On this basis, we execute 3D modeling operations, like extrusion, lofting or sweeping, to generate the 3D models of blocks. The final accurate 3D models are generated by applying the union Boolean operations over the block models. We evaluate our reconstruction method on a variety of raw LiDAR scans to verify its robustness and effectiveness.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer-Aided Design - Volume 93, December 2017, Pages 1-14
Journal: Computer-Aided Design - Volume 93, December 2017, Pages 1-14
نویسندگان
Cheng Yi, Yuan Zhang, Qiaoyun Wu, Yabin Xu, Oussama Remil, Mingqiang Wei, Jun Wang,