Browsing by Author "Chen, Wen-Chao"

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3D robust reconstruction using a hand-held digital camera
(2008)
Chen, Zen
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Wu, Chang-Hao
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Chen, Wen-Chao
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資訊工程學系
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Department of Computer Science
Conventional vision-based reconstruction methods comprise three main steps: feature extraction and matching, estimation of camera matrices, and triangulation method for 3D point computation. Standard factorization algorithms provide an effective means for computing the 3D object shape and camera models simultaneously from the multiple views without going through the process of pairwise reconstructions. Since the numbers of features used in all views must be the same and sufficiently large in these methods, they are not applicable to the cases where not every feature point is visible in all views. On the other hand, the extracted features are often corrupted by image noise. Therefore, there is a need for a reconstruction algorithm that can handle missing feature points and image noise both in order to yield a robust result. We propose a method integrating a multi-view reconstruction and a least-squared-error estimation to produce a robust reconstruction result. Besides, the proposed method utilizes an auto-calibration scheme to convert the projective reconstruction to a Euclidean reconstruction. The method is tested on the synthetic and real data.
A NOVEL 3D DENSE RECONSTRUCTION WITH HIGH ACCURACY AND COMPLETENESS
(2013)
Chen, Zen
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Chen, Wen-Chao
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Sung, Ping-Yi
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資訊工程學系
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Department of Computer Science
This paper presents a planar patch based method for 3D dense reconstruction. The object surface is represented as a point cloud. For high reconstruction accuracy and completeness, our strategy is to fit the object surface by planar patches at different scales. Furthermore, we apply adaptive weights to defme the photo-consistency measures for view matching. To optimize the view matching, we adopt a derivative-free GLN-PSO stochastic optimization method. Finally, to improve the reconstruction quality we design a patch priority queue for ordering the seed patches for patch expansion. The experimental results indicate the current implementation results are generally superior or comparable in comparison with the top ranked reconstruction methods reported in the Middlebury MVS website.
A Performance Controllable Octree Construction Method
(2008)
Chen, Zen
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Chou, Hong-Long
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Chen, Wen-Chao
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資訊工程學系
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Department of Computer Science
The conventional octree construction method is implemented iteratively at consecutive subdivision levels. The resultant octree models at different subdivision levels contain quite different octant compositions, so the system performance, in terms of model accuracy, memory space and construction time, changes widely with the subdivision level number. Since the big system performance gap is not desirable for the practical application, new construction methods must have a finer control over the system performance. In the paper a new construction method is proposed using new types of octree octants. Interesting properties and computer simulations of the new method are presented. The performance comparison between the conventional method and the new method is made under the comparable XOR projection error condition. It is shown that under this condition the memory space and construction time required by the conventional method can be reduced significantly by the new method.
A quality controllable multi-view object reconstruction method for 3D imaging systems
(2010-07-01)
Chen, Wen-Chao
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Chou, Hong-Long
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Chen, Zen
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資訊工程學系
;
Department of Computer Science
This paper addresses a novel multi-view visual hull mesh reconstruction for 3D imaging with a system quality control capability. There are numerous 3D imaging methods including multi-view stereo algorithms and various visual hull/octree reconstruction methods known as modeling from silhouettes. The octree based reconstruction methods are conceptually simple to implement, while encountering a conflict between model accuracy and memory size. Since the tree depth is discrete, the system performance measures (in terms of accuracy, memory size, and computation time) are generally varying rapidly with the pre-specified tree depth. This jumping system performance is not suitable for practical applications; a desirable 3D reconstruction method must have a finer control over the system performance. The proposed method aims at the visual quality control along with better management of memory size and computation time. Furthermore, dynamic object modeling is made possible by the new method. Also, progressive transmission of the reconstructed model from coarse to fine is provided. The reconstruction accuracy of the 3D model acquired is measured by the exclusive OR (XOR) projection error between the pairs of binary images: the reconstructed silhouettes and the true silhouettes in the multiple views. Interesting properties of the new method and experimental comparisons with other existing methods are reported. The performance comparisons are made under either a comparable silhouette inconsistency or a similar triangle number of the mesh model. It is shown that under either condition the new method requires less memory size and less computation time. (C) 2010 Published by Elsevier Inc.
Cone-stacked carbon nanofibers with cone angle increasing along the longitudinal axis
(2007-02-01)
Lin, Cheng-Te
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Chen, Wen-Chao
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Yen, Ming-Yu
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Wang, Lung-Shen
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Lee, Chi-Young
Using porous anodic aluminum oxide as template and petroleum pitch as precursor, a massive amount of uniform carbon nanofibers was obtained after thermal treatment. The diameter and length were 300 nm and 60 pm, respectively. The difference between these and the classic herringbone structure is that the angle between the graphenes and the fiber axis increases regularly along the axis instead of being fixed. TEM observations show that the nanofiber consists of stacked conical graphenes with cone angles that steadily increase from 60 degrees to 180 degrees along the fiber axis. This structure is the first to be produced without using catalytic CVD, and has not been reported using template procedures. The large deformation of the graphene planes at the tip of the nanofiber may produce interesting electronic applications. (c) 2006 Elsevier Ltd. All rights reserved.
Stochastic Optimization Based 3D Dense Reconstruction from Multiple Views with High Accuracy and Completeness
(2015-01-01)
Chen, Wen-Chao
;
Chen, Zen
;
Sung, Ping-Yi
;
資訊工程學系
;
Department of Computer Science
This paper presents a stochastic optimization based 3D dense reconstruction from multiple views. Accuracy and completeness are two major measure indices for performance evaluation of various multi-view stereo (MVS) algorithms. First, the reconstruction accuracy is highly related to the stereo mismatches over the multiple views. Stereo mismatches occur in the image regions involving the lack of texture, depth discontinuity, or repeated texture patterns. Second, an insufficient number of views or occlusions between objects also lead to the difficulty in matching so that the reconstruction completeness degrades. In pursuit of high accuracy and completeness we present the appropriate techniques to solve the above problems in the reconstruction task. To deal with the various stereo mismatch problems we propose to apply adaptive matching functions and allow partial matching. We shall model the object to be reconstructed by a set of 3D oriented planar patches covering the visible object surface. The adopted multi-view reconstruction is formulated as a patch expansion process under a tree hierarchy. In order to find the optimal patches via multi-view stereo matching we shall employ a PSO (Particle Swarm Optimization) method for the sake of implementation simplicity and avoidance of possible local traps as found in the derivative based optimization methods. The success in the PSO method relies on imposing proper constraints on ranges of the patch parameters including the patch depth and patch normal vector which are involved in the PSO objective function (i.e., the stereo matching function). Furthermore, we use a varying patch size to obtain the reliable patches in the areas containing less texture, repeated texture pattern, or depth discontinuity. To secure a high reconstruction quality we advocate a patch priority queue to select the best patch during the patch expansion. All of the above mentioned techniques are also effective in the situations when the number of views is sparse or the camera baseline width is wide. The proposed method is tested on synthetic and real image data sets. The experimental results indicate that the proposed method is superior or comparable to the top ranked reconstruction methods reported in the public Middlebury MVS evaluation website.