Parallel surface reconstruction for large‐scale scenes in the wild

Abstract

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.