Recovering a high‐quality texture model in a short time still a pursued goal in both computer vision and graphics communities. Thus, in the past decade, several methods have been proposed for fitting a texture model from the dense point clouds. However, these methods are computational intensity and also suffer from noise especially in outdoor. Moreover, with the popularization of Unmanned Aerial Vehicles (UAVs), it is getting easier to capture image data. While modern methods have much novelty, they may spend a long time on big image datasets. To accelerate the process of texture modeling, in this paper we present a parallel approach to fitting a texture model from the dense point clouds. The presented method makes use of the parallel computing technology and is implemented in parallel octree structure as well as parallel marching cubes. Finally, we conduct a comprehensive experiment on several benchmarking datasets and experimental results show that our method outperforms the state‐of‐the‐art methods and has also a 20 times acceleration.

中文翻译：

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野外大规模场景的并行表面重建

在计算机视觉和图形社区中，在短时间内恢复高质量的纹理模型仍然是一个追求的目标。因此，在过去的十年中，已经提出了几种用于从密集点云中拟合纹理模型的方法。但是，这些方法具有计算强度，并且特别是在室外，还会受到噪声的影响。此外，随着无人机的普及，捕获图像数据变得越来越容易。尽管现代方法具有许多新颖性，但它们可能会在大型图像数据集上花费很长时间。为了加快纹理建模的过程，在本文中，我们提出了一种从密集点云中拟合纹理模型的并行方法。提出的方法利用了并行计算技术，并在并行八叉树结构以及并行行进立方体中实现。最后，