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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.