Texture Optimization Algorithm For 3D Scenes Fusing Semantic - Grayscale Features

In order to address the problem of blurring artifacts in 3D scene texture mapping, this paper proposes a texture optimization algorithm by fusing semantic features and grayscale features for 3D scene texture optimization, which can recover photorealistic texture maps for 3D scenes using multi-view images. Compared to the existing algorithm, this algorithm has obvious advantages for 3D scene texture mapping with large camera pose errors and low precision reconstruction geometry. We first calculate the initial images mapping relationship based on the camera pose corresponding to the image. Then, the initial mapping relationship is optimized by fusing semantic features to ensure the correct color of the geometric models in the scene, and further optimized by fusing grayscale features to ensure the correct color of the texture within the geometric models. Finally, the texture images are synthesized by fusing the pixels with the 3D scene information using a weighted averaging strategy, and the texture images are back-projected onto the geometry according to the mapping relationship, which generates a 3D scene with high-fidelity texture. We test our algorithm using 3D mesh with the lack of color information as data, compared to the existing algorithm, The experimental results show that the algorithm can generate 3D scenes with clear and high-fidelity textures.