Large-Scale Architectural Asset Extraction from Panoramic Imagery

被引：5

作者：

Zhu, Peihao ^{[1
]}

Para, Wamiq Reyaz ^{[1
]}

Fruhstuck, Anna ^{[1
]}

Femiani, John ^{[2
]}

Wonka, Peter ^{[1
]}

机构：

[1] King Abdullah Univ Sci & Technol, Dept Comp Sci, Jeddah 23955, Saudi Arabia

[2] Miami Univ, Coll Engn & Comp, Oxford, OH 45056 USA

来源：

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS | 2022年 / 28卷 / 02期

关键词：

Data mining; Object detection; Solid modeling; Urban planning; Microsoft Windows; Visualization; Three-dimensional displays; Asset extraction; urban modeling; object detection; clustering; semantic segmentation;

D O I：

10.1109/TVCG.2020.3010694

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

We present a system to extract architectural assets from large-scale collections of panoramic imagery. We automatically rectify and crop parts of the panoramic image that contain dominant planes, and then use object detection to extract assets such as facades and windows. We also provide various tools to identify attributes of the assets to determine the asset quality and index the assets for search. In addition, we propose a User Interface (UI) to visualize and query assets. Finally, we present applications for urban modeling and texture synthesis.

引用

页码：1301 / 1316

页数：16

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