Kinect-based easy 3D object reconstruction

Inspired by the recently developed KinectFusion technique, which is able to reconstruct a 3D scene in real time through moving Kinect, we consider improving KinectFusion for 3D reconstruction of a real object. We make some adaptations to KinectFusion so as to identify the object-of-interest and sepa...

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Main Authors: Cham Tat Jen (SCE), Xu, Di, Cai, Jianfei, Fu, Philip, Zhang, Juyong
Other Authors: School of Computer Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/99335
http://hdl.handle.net/10220/17250
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-993352020-05-28T07:18:04Z Kinect-based easy 3D object reconstruction Cham Tat Jen (SCE) Xu, Di Cai, Jianfei Fu, Philip Zhang, Juyong School of Computer Engineering IEEE Pacific Rim Conference on Multimedia (13th : 2012 : Singapore) DRNTU::Engineering::Computer science and engineering Inspired by the recently developed KinectFusion technique, which is able to reconstruct a 3D scene in real time through moving Kinect, we consider improving KinectFusion for 3D reconstruction of a real object. We make some adaptations to KinectFusion so as to identify the object-of-interest and separate the 3D object model from the entire 3D scene. Moreover, considering that the 3D object model generated by KinectFusion often contains some clearly visible outliers due to the noisy Kinect data, we propose a refinement scheme to remove the outliers. Our basic idea is to make use of the existing powerful 2D segmentation tool to refine the silhouette in each color image and then form visual hull via the refined dense silhouettes to improve the 3D object model. Experimental results show improved performance. 2013-11-05T04:45:15Z 2019-12-06T20:06:11Z 2013-11-05T04:45:15Z 2019-12-06T20:06:11Z 2012 2012 Conference Paper Xu, D., Cai, J., Cham, T. J., Fu, P., & Zhang, J. (2012). Kinect-Based Easy 3D Object Reconstruction. 13th Pacific-Rim Conference on Multimedia, PCM 2012, 7674, 476-483. https://hdl.handle.net/10356/99335 http://hdl.handle.net/10220/17250 10.1007/978-3-642-34778-8_44 en
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Cham Tat Jen (SCE)
Xu, Di
Cai, Jianfei
Fu, Philip
Zhang, Juyong
Kinect-based easy 3D object reconstruction
description Inspired by the recently developed KinectFusion technique, which is able to reconstruct a 3D scene in real time through moving Kinect, we consider improving KinectFusion for 3D reconstruction of a real object. We make some adaptations to KinectFusion so as to identify the object-of-interest and separate the 3D object model from the entire 3D scene. Moreover, considering that the 3D object model generated by KinectFusion often contains some clearly visible outliers due to the noisy Kinect data, we propose a refinement scheme to remove the outliers. Our basic idea is to make use of the existing powerful 2D segmentation tool to refine the silhouette in each color image and then form visual hull via the refined dense silhouettes to improve the 3D object model. Experimental results show improved performance.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Cham Tat Jen (SCE)
Xu, Di
Cai, Jianfei
Fu, Philip
Zhang, Juyong
format Conference or Workshop Item
author Cham Tat Jen (SCE)
Xu, Di
Cai, Jianfei
Fu, Philip
Zhang, Juyong
author_sort Cham Tat Jen (SCE)
title Kinect-based easy 3D object reconstruction
title_short Kinect-based easy 3D object reconstruction
title_full Kinect-based easy 3D object reconstruction
title_fullStr Kinect-based easy 3D object reconstruction
title_full_unstemmed Kinect-based easy 3D object reconstruction
title_sort kinect-based easy 3d object reconstruction
publishDate 2013
url https://hdl.handle.net/10356/99335
http://hdl.handle.net/10220/17250
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