3D Reconstruction by Automatic Object Segmentation fromMulti-view image

• Main Authors: Ying-Hsuang Wang, 王映萱
• Other Authors: 莊永裕
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/97118331436348769058

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 103 === As 3D printing technique becomes more popular, the requirements of 3D models also increase. However, even for an experienced expert, making a 3D model from real world object takes a long time, and needless to say, it’s not an easy task for people without any background knowledge. In this thesis, we propose an approach that allows arbitrary users to create their own 3D models without any experience and background knowledge. First, we develop a guidance application on mobile device which guides users to take sufficient images from the target object. Second, in order to avoid the background being reconstructed as part of the 3D models, we design an automatic object segmentation method to separate foreground and background in multi-view image. Third, we use the segmentation masks to make a visual hull as our final output. The key behind our approach is a MRF framework that combines foreground/background appearance model, epipolar geometry constraints, and feature matching constraints into a single energy function. Therefore, we can use graph cut algorithm to efficiently minimize this function and get the segmentation result. We create a visual hull of the object from the segmentation masks, and then back-projecting it to all the images to make the silhouettes consistent in all view. The consistent silhouettes are used to update our foreground appearance model. We iteratively apply graph cut step and the update step until the segmentation converges. Our method is able to reconstruct a texture-less object, which remains a challenge for most of MVS algorithm. In addition, by taking color and spatial constraints into concern, our approach can separate foreground and background that are overlapping in color space, which is difficult for the traditional object segmentation method.