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CLC number: TP391.4

On-line Access: 2016-05-04

Received: 2015-09-28

Revision Accepted: 2016-02-21

Crosschecked: 2016-04-18

Cited: 0

Clicked: 1889

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Chu-hua Huang

http://orcid.org/0000-0003-3507-4256

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.5 P.422-434

http://doi.org/10.1631/FITEE.1500316


A multiscale-contour-based interpolation framework for generating a time-varying quasi-dense point cloud sequence


Author(s):  Chu-hua Huang, Dong-ming Lu, Chang-yu Diao

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   diaochangyu@gmail.com

Key Words:  Multi-view video, Free-viewpoint video, Point-pair, Multiscale-contour-based interpolation, Spatio-temporal-contour, Consistency, Time-varying point cloud sequence


Chu-hua Huang, Dong-ming Lu, Chang-yu Diao. A multiscale-contour-based interpolation framework for generating a time-varying quasi-dense point cloud sequence[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(5): 422-434.

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Abstract: 
To speed up the reconstruction of 3D dynamic scenes in an ordinary hardware platform, we propose an efficient framework to reconstruct 3D dynamic objects using a multiscale-contour-based interpolation from multi-view videos. Our framework takes full advantage of spatio-temporal-contour consistency. It exploits the property to interpolate single contours, two neighboring contours which belong to the same model, and two contours which belong to the same view at different times, corresponding to point-, contour-, and model-level interpolations, respectively. The framework formulates the interpolation of two models as point cloud transport rather than non-rigid surface deformation. Our framework speeds up the reconstruction of a dynamic scene while improving the accuracy of point-pairing which is used to perform the interpolation. We obtain a higher frame rate, spatio-temporal-coherence, and a quasi-dense point cloud sequence with color information. Experiments with real data were conducted to test the efficiency of the framework.

The proposed method has limited complexity which is good for the intended use (e.g., real-time rendering or at least higher frame rate). On the other hand, the method is still far from being real-time (e.g., 50 seconds/frame) and the degree of scientific innovation is somehow limited as well. On the other hand, the paper is well structured on experiments were performed on real data sets.

基于多尺度轮廓插值生成准密集时变点云模型序列

目的:提高采用多视角视频序列生成时变三维模型序列的速度,以更好地跟踪三维动态物体的形变过程;同时保证时变三维模型序列的外观质量。
创新点:提出基于多尺度轮廓插值生成时变点云模型序列的方法。利用重建对象轮廓的多尺度时空连续性,完成时变三维模型序列的时空维度插值。
方法:首先,采用基于剪影轮廓原理重建物体关键帧的稀疏三维模型。接着,分析三维模型的轮廓点在点级别、轮廓级别、模型级别的连续性,并在该过程中采用距离图插值来增强轮廓的连续性。然后,采用最近点查找方法获得匹配点对,在三个尺度上对匹配点对进行线性密集化。最后,生成具有良好时空一致性的准密集时变三维模型序列。
结论:利用轮廓多尺度时空连续性能够提高重建对象的形变跟踪速度,且时变三维模型序列具有良好的外观质量。

关键词:多视角视频序列;自由视点视频序列;匹配点对;多尺度轮廓插值;时空轮廓;一致性;时变点云模型序列

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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