Web-based Deep Segmentation of Building Structure

Graduate Thesis Or Dissertation

Public Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/0c483q94d

Descriptions

Attribute Name	Values
Creator	Chen, Zehuan
Abstract	Deep learning and neural network has been widely used in research, deep learning has empowered many tasks such as point clouds segmentation and shape recognition. One of the main advantages of deep interaction point cloud segmentation is that it allows the feature extraction can be learned through neural network based on a large amount of dataset. Our focus is large point clouds, we propose a variety of measuring tools to analyze and validate raw point cloud data, which is the web-based deep segmentation user interaction on large point clouds. It allows users to view data sets with millions of points, from sources such as building structure and indoor scene, in standard web browsers, and processing 3D point clouds deep segmentation with the neural network. The interaction tools can assist to distinguish building structure and non-building structure in one room.
Contributor	Che, Erzhuo Wu, Wenxuan
License	All rights reserved
Resource Type	Masters Thesis
Date Issued	2019-01-10
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Computer Science
Degree Grantor	Oregon State University
Commencement Year	2019
Advisor	Li, Fuxin
Committee Member	Turkan, Yelda Olsen, Michael J. de Amicis, Raffaele
Academic Affiliation	Electrical Engineering and Computer Science
Rights Statement	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]

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