Premium
Parallel surface reconstruction for large‐scale scenes in the wild
Author(s) -
Cao Mingwei,
Gao Hao,
Jia Wei
Publication year - 2021
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.2953
Subject(s) - computer science , point cloud , texture synthesis , artificial intelligence , octree , computer vision , process (computing) , computer graphics , benchmarking , noise (video) , novelty , texture (cosmology) , benchmark (surveying) , scale (ratio) , image texture , computer graphics (images) , image (mathematics) , image processing , philosophy , theology , geodesy , marketing , geography , business , operating system , physics , quantum mechanics
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.