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Constructing a 3D trunk model from two images

机译:从两个图像构造3D树干模型

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Trees stand for a key component in the natural environment, thus modeling realistic trees has received much attentions of researchers in computer graphics. However, most trees in computer graphics are generated according to some procedural rules in conjunction with some random perturbations, thus they are generally different from the real trees in the natural environment. In this paper, we propose a systematic approach to create a 3D trunk graphical model from two images so that the created trunk has a similar 3D trunk structure to the real one. In the proposed system, the trunk is first segmented from the image via an interactive segmentation tool and its skeleton is then extracted. Some points on the skeleton are selected and their context relations are established for representing the 2D trunk structure. A camera self-calibration algorithm appropriate for the two-view case is developed, and a minimum curvature constraint is employed to recover the 3D trunk skeleton from the established 2D trunk structure and the calibrated camera. The trunk is then modeled by a set of generalized cylinders around the recovered 3D trunk skeleton. A polygonal mesh representing the trunk is finally generated and a textured 3D trunk model is also produced by mapping the image onto the surface of the 3D trunk model. We have conducted some experiments and the results demonstrated that the proposed system can actually yield a visually plausible 3D trunk model which is similar to the real one in the image, (C) 2006 Elsevier Inc. All rights reserved.
机译:树木是自然环境中的重要组成部分,因此,对逼真的树木进行建模已引起计算机图形学研究人员的广泛关注。但是,计算机图形学中的大多数树是根据一些程序规则以及一些随机扰动生成的,因此它们通常与自然环境中的真实树不同。在本文中,我们提出了一种系统的方法,该方法可从两个图像创建3D干线图形模型,以使创建的干线具有与真实图像相似的3D干线结构。在提出的系统中,首先通过交互式分割工具从图像中分割树干,然后提取其骨架。选择骨架上的一些点,并建立它们的上下文关系以表示2D树干结构。开发了一种适用于两视图情况的相机自校准算法,并采用最小曲率约束从已建立的2D行李箱结构和已校准的相机中恢复3D行李箱骨架。然后,通过围绕恢复的3D树干骨架的一组广义圆柱体对树干进行建模。最后生成代表躯干的多边形网格,并通过将图像映射到3D躯干模型的表面来生成带纹理的3D躯干模型。我们进行了一些实验,结果表明,提出的系统实际上可以产生一个视觉上合理的3D主干模型,该模型与图像中的真实主干模型相似,(C)2006 Elsevier Inc.保留所有权利。

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