Browsing by Author "Sung, Ping-Yi"

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A NOVEL 3D DENSE RECONSTRUCTION WITH HIGH ACCURACY AND COMPLETENESS
(2013)
Chen, Zen
;
Chen, Wen-Chao
;
Sung, Ping-Yi
;
資訊工程學系
;
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.
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.