We present a data-driven approach that colorizes 3D furniture models and
indoor scenes by leveraging indoor images on the internet. Our approach is able
to colorize the furniture automatically according to an example image. The core
is to learn image-guided mesh segmentation to segment the model into different
parts according to the image object. Given an indoor scene, the system supports
colorization-by-example, and has the ability to recommend the colorization
scheme that is consistent with a user-desired color theme. The latter is
realized by formulating the problem as a Markov random field model that imposes
user input as an additional constraint. We contribute to the community a
hierarchically organized image-model database with correspondences between each
image and the corresponding model at the part-level. Our experiments and a user
study show that our system produces perceptually convincing results comparable
to those generated by interior designers.

该文研究了基于数据驱动的方法对 3D 家具模型和室内场景进行着色，并使用互联网上的室内图像进行引导。使用图像引导网格分割将模型分成不同的部分，以提高着色效果，并以马尔可夫随机场模型实现一致的着色方案。实验结果表明，该系统可以产生与室内设计师生成的结果相媲美且具有说服力的结果。