A method of using image-view pairs to represent complex 3D objects

Manually building models of complex objects is usually very difficult. 3D scanning can provide accurate models, but the required processing of very large data sets is time-consuming and not suitable for real-time applications. This paper presents a novel method that uses a series of images to repres...

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Main Authors: Lihong Luo, Jianqing Mo, Xian Yang
Format: Article
Language:English
Published: Taylor & Francis Group 2018-01-01
Series:Cogent Engineering
Subjects:
Online Access:http://dx.doi.org/10.1080/23311916.2018.1491264
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author Lihong Luo
Jianqing Mo
Xian Yang
author_facet Lihong Luo
Jianqing Mo
Xian Yang
author_sort Lihong Luo
collection DOAJ
description Manually building models of complex objects is usually very difficult. 3D scanning can provide accurate models, but the required processing of very large data sets is time-consuming and not suitable for real-time applications. This paper presents a novel method that uses a series of images to represent an object. Initially, we obtain many images by photographing in different spatial directions. Each image matches a vector of an observed direction. During real-time rendering, the image closest to the eye direction is selected and shown. We call this method of model building the image-view pairs (IVP) model. The IVP model can support dynamic shadows in addition to static shadows. This paper proposes a method based upon affine transformation and multitexturing. IVP objects also support collision detection. Experiments show that the proposed IVP objects look realistic and require less processing power and memory. Moreover, the displayed scene runs very smoothly. The proposed method can be very useful for large real-time applications.
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spelling doaj.art-d9cfc42ed0f249abb69dcfe0fecc42162023-08-02T06:14:20ZengTaylor & Francis GroupCogent Engineering2331-19162018-01-015110.1080/23311916.2018.14912641491264A method of using image-view pairs to represent complex 3D objectsLihong Luo0Jianqing Mo1Xian Yang2Guangdong University of TechnologyGuangdong University of TechnologyGuangdong University of TechnologyManually building models of complex objects is usually very difficult. 3D scanning can provide accurate models, but the required processing of very large data sets is time-consuming and not suitable for real-time applications. This paper presents a novel method that uses a series of images to represent an object. Initially, we obtain many images by photographing in different spatial directions. Each image matches a vector of an observed direction. During real-time rendering, the image closest to the eye direction is selected and shown. We call this method of model building the image-view pairs (IVP) model. The IVP model can support dynamic shadows in addition to static shadows. This paper proposes a method based upon affine transformation and multitexturing. IVP objects also support collision detection. Experiments show that the proposed IVP objects look realistic and require less processing power and memory. Moreover, the displayed scene runs very smoothly. The proposed method can be very useful for large real-time applications.http://dx.doi.org/10.1080/23311916.2018.1491264virtual reality3d modelingreal-time renderingimage modeling
spellingShingle Lihong Luo
Jianqing Mo
Xian Yang
A method of using image-view pairs to represent complex 3D objects
Cogent Engineering
virtual reality
3d modeling
real-time rendering
image modeling
title A method of using image-view pairs to represent complex 3D objects
title_full A method of using image-view pairs to represent complex 3D objects
title_fullStr A method of using image-view pairs to represent complex 3D objects
title_full_unstemmed A method of using image-view pairs to represent complex 3D objects
title_short A method of using image-view pairs to represent complex 3D objects
title_sort method of using image view pairs to represent complex 3d objects
topic virtual reality
3d modeling
real-time rendering
image modeling
url http://dx.doi.org/10.1080/23311916.2018.1491264
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