Abstract
The Markov Random Field (MRF) energy function, constructed by existing OpenMVS-based 3D texture reconstruction algorithms, considers only the image label of the adjacent triangle face for the smoothness term and ignores the planar-structure information of the model. As a result, the generated texture charts results have too many fragments, leading to a serious local miscut and color discontinuity between texture charts. This paper fully utilizes the planar structure information of the mesh model and the visual information of the 3D triangle face on the image and proposes an improved, faster, and high-quality texture chart generation method based on the texture chart generation algorithm of the OpenMVS. This methodology of the proposed approach is as follows: (1) The visual quality on different visual images of each triangle face is scored using the visual information of the triangle face on each image in the mesh model. (2) A fully automatic Variational Shape Approximation (VSA) plane segmentation algorithm is used to segment the blocked 3D mesh models. The proposed fully automatic VSA-based plane segmentation algorithm is suitable for multi-threaded parallel processing, which solves the VSA framework needed to manually set the number of planes and the low computational efficiency in a large scene model. (3) The visual quality of the triangle face on different visual images is used as the data term, and the image label of adjective triangle and result of plane segmentation are utilized as the smoothness term to construct the MRF energy function. (4) An image label is assigned to each triangle by the minimizing energy function. A texture chart is generated by clustering the topologically-adjacent triangle faces with the same image label, and the jagged boundaries of the texture chart are smoothed. Three sets of data of different types were used for quantitative and qualitative evaluation. Compared with the original OpenMVS texture chart generation method, the experiments show that the proposed approach significantly reduces the number of texture charts, significantly improves miscuts and color differences between texture charts, and highly boosts the efficiency of VSA plane segmentation algorithm and OpenMVS texture reconstruction.
Highlights
Over the last two decades, 3D modeling from low-altitude oblique images has been used in a wide range of applications, including urban planning, tourism, and computer vision. 3D image-modeling technology primarily involves generating two kinds of reconstruction, the 3D model object [1,2,3,4,5] and its texture
(3) The visual quality of the triangle face on different visual images is used as the data term, and the image label of adjective triangle and result of plane segmentation are utilized as the smoothness term to construct the Markov Random Field (MRF) energy function
Compared with the original Open Multi-View Stereovision (OpenMVS) texture chart generation method, the experiments show that the proposed approach significantly reduces the number of texture charts, significantly improves miscuts and color differences between texture charts, and highly boosts the efficiency of Variational Shape Approximation (VSA) plane segmentation algorithm and OpenMVS texture reconstruction
Summary
Over the last two decades, 3D modeling from low-altitude oblique images has been used in a wide range of applications, including urban planning, tourism, and computer vision. 3D image-modeling technology primarily involves generating two kinds of reconstruction, the 3D model object [1,2,3,4,5] and its texture. The purpose of the smoothness term is to reduce the number of texture charts and the overall color discontinuity, which can effectively improve the efficiency of subsequent texture mapping steps, and even have a better promotion effect on the future application of real-time dynamic local texture mapping visualization (3D texture model visualization) It can minimize the sampling error of the texture chart boundary seam-line to a certain extent. This paper fully utilizes the planar structure information of the mesh model and the visual information of the 3D triangle face on the image and provides an improved, faster, and high-quality texture chart generation method based on the OpenMVS texture chart generation algorithm. Using the method proposed in this paper, the number of generated texture chart fragments greatly reduces, which will help solve the problem of miscutting and large color discontinuity between texture charts, and will help to improve the efficiency of subsequent texture mapping steps and the visual expression of 3D texture models.
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