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3D colored model generation based on multiview textures and triangular mesh

机译:基于多视图纹理和三角形网格的3D彩色模型生成

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Three-dimensional colored models are of great interests to many fields. With the growing availability of inexpensive 3D sensing systems, it is easy to obtain triangular mesh and multiview textures. These range and vision data can be fused to provide such 3D colored models. However, low-cost sensing generates various noise components involving low-quality texture, errors in calibration and mesh modeling. Our primary objective is to establish high-quality 3D colored models on the basis of mesh and textures, while considering the noise types and characteristics. In this paper, we contribute in two ways. The first contribution is a point-based algorithm to color 3D models, where 3D surface points are used as primitives to process and store color information. The algorithm features three novel techniques: (a) accurate depth image estimation, (b) adaptive 3D surface point upsampling and (c) texture blending using those points. The algorithm provides colored models as dense colored point clouds, which can be rendered with various standard techniques for visualization. Our second contribution is an algorithm for textured model rendering, where blended textures are generated and mapped onto the mesh. The experimental results show that our algorithms efficiently provide high-quality colored models and enable visually appealing rendering, while being tolerant to errors from data acquisition. We also quantify the efficiency of our point upsampling algorithm with novel metrics assessing the influence of the 3D points.
机译:三维彩色模型在许多领域都引起了极大的兴趣。随着廉价3D传感系统的可用性不断提高,很容易获得三角形网格和多视图纹理。这些距离和视觉数据可以融合以提供此类3D彩色模型。但是,低成本传感会产生各种噪声成分,其中包括低质量的纹理,校准和网格建模中的错误。我们的主要目标是在考虑噪声类型和特征的同时,基于网格和纹理建立高质量的3D彩色模型。在本文中,我们通过两种方式做出贡献。第一项贡献是对3D模型进行着色的基于点的算法,其中3D表面点用作处理和存储颜色信息的基元。该算法具有三种新颖的技术:(a)精确的深度图像估计,(b)自适应3D表面点上采样和(c)使用这些点进行纹理融合。该算法将彩色模型提供为密集的彩色点云,可以使用各种标准技术对其进行可视化渲染。我们的第二个贡献是用于纹理模型渲染的算法,其中生成混合纹理并将其映射到网格上。实验结果表明,我们的算法有效地提供了高质量的彩色模型,并能够在视觉上吸引人的渲染,同时容忍来自数据采集的错误。我们还通过评估3D点影响的新颖指标来量化我们的点上采样算法的效率。

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