  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.
