Large-scale and Efficient Texture Mapping Algorithm via Loopy Belief
  Propagation
Xiao ling Rongjun Qin
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.