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B-REP OBJECT DESCRIPTION FROM MULTIPLE RANGE VIEWS

机译:来自多个视角的B-REP对象描述

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We present a system which computes an integrated description of an object from multiple range images. The object description is in the form of B-rep (boundary representation), which has not been achieved by the computer vision community. To do so, we emphasize the inherent difficulties and ambiguities in the low to mid level vision, and present novel techniques of resolving them. In this system, each view of the object is represented as an attributed graph, where nodes correspond to the surfaces (vertices) and links represent the relationship between surfaces. The main issue in surface extraction is contour closure, which is formulated as a dynamic network. The underlying principle for this network is weak smoothness and geometric cohesion, and is modeled as the interaction between long and short term variables. Long term variables represent the initial boundary grouping computed from the low level surface features, and short term variables represent the competing hypotheses that cooperate with the long term variables. The matching problem involves matching visible surfaces and vertices, and provides the necessary basis for volumetric reconstruction from multiple views. The matching strategy is a two step process, where in each step uses the Hopfield network. At each step, we specify a set of local, adjacency and global constraints, and define an appropriate energy function to be minimized. At the first level of this hierarchy, surface patches are matched and the rigidity transformation is computed. At the second level, the mapping is refined by matching the corresponding vertices, and the transformation is verified. The multiple-view reconstruction consists of two steps. First, we build a composite graph that contains the bounding surfaces and their corresponding attributes, and then intersect these surfaces so that the edges and vertices corresponding to the B-rep description are identified. We present results on objects with planar, as well as quadratically-curved surfaces. [References: 72]
机译:我们提出了一种系统,该系统可以从多个距离图像中计算出一个对象的综合描述。对象描述采用B-rep(边界表示)的形式,计算机视觉社区尚未实现。为此,我们强调中低级视觉的内在困难和歧义,并提出解决这些问题的新颖技术。在此系统中,对象的每个视图均表示为属性图,其中节点对应于曲面(顶点),链接表示曲面之间的关系。曲面提取中的主要问题是轮廓闭合,该轮廓闭合被公式化为动态网络。该网络的基本原理是弱平滑性和几何凝聚力,并被建模为长期和短期变量之间的相互作用。长期变量表示根据低层表面特征计算出的初始边界分组,而短期变量表示与长期变量配合使用的相互竞争的假设。匹配问题涉及匹配可见表面和顶点,并为从多个视图进行体积重建提供了必要的基础。匹配策略是一个两步过程,其中每一步都使用Hopfield网络。在每个步骤中,我们指定一组局部约束,邻接约束和全局约束,并定义要最小化的适当能量函数。在此层次结构的第一级,匹配曲面补丁并计算刚度转换。在第二级,通过匹配相应的顶点来精简映射,并验证变换。多视图重建包括两个步骤。首先,我们构建一个包含边界表面及其对应属性的合成图,然后将这些表面相交,以便标识与B-rep描述相对应的边和顶点。我们介绍了具有平面以及二次曲面的对象的结果。 [参考:72]

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