Texture mapping as a fundamental task in 3D modeling has been well established for well-acquired aerial assets under consistent illumination, yet it remains a challenge when it is scaled to large datasets with images under varying views and illuminations. A well-performed texture mapping algorithm must be able to efficiently select views, fuse and map textures from these views to mesh models, at the same time, achieve consistent radiometry over the entire model. Existing approaches achieve efficiency either by limiting the number of images to one view per face, or simplifying global inferences to only achieve local color consistency. In this paper, we break this tie by proposing a novel and efficient texture mapping framework that allows the use of multiple views of texture per face, at the same time to achieve global color consistency. The proposed method leverages a loopy belief propagation algorithm to perform an efficient and global-level probabilistic inferences to rank candidate views per face, which enables face-level multi-view texture fusion and blending. The texture fusion algorithm, being non-parametric, brings another advantage over typical parametric post color correction methods, due to its improved robustness to non-linear illumination differences. The experiments on three different types of datasets (i.e. satellite dataset, unmanned-aerial vehicle dataset and close-range dataset) show that the proposed method has produced visually pleasant and texturally consistent results in all scenarios, with an added advantage of consuming less running time as compared to the state of the art methods, especially for large-scale dataset such as satellite-derived models.

翻译：纹理映射作为三维建模中的基础任务，在光照一致且数据采集良好的空中影像场景中已有成熟方案，但在面对包含不同视角与光照条件的大规模数据集时仍然存在挑战。一个优秀的纹理映射算法需要能够高效选择视角、融合并将纹理映射至网格模型，同时在整个模型上实现辐射度一致性。现有方法要么通过限制每个面仅使用单视角图像来提升效率，要么简化全局推理以仅实现局部色彩一致性。本文提出一种新颖且高效的纹理映射框架，突破这一局限：在允许每个面使用多视角纹理数据的同时，实现全局色彩一致性。该方法利用环状置信传播算法执行高效的全局概率推理，为每个面片完成候选视角排序，进而实现面级多视角纹理融合与混合。由于该纹理融合算法采用非参数化方式，相较于典型的参数化后期色彩校正方法，对非线性光照差异具有更强的鲁棒性。在三类不同数据集（卫星影像、无人机影像及近景影像）上的实验表明：本方法在所有场景下均能生成视觉协调且纹理一致的成果，尤其在处理卫星模型等大规模数据集时，相较于现有最优方法具有运行时间更短的优势。