A Data-driven Approach for Furniture and Indoor Scene Colorization
This addresses the problem of automating interior design colorization for users, but it is incremental as it builds on existing data-driven and MRF methods.
The paper tackles automatic colorization of 3D furniture models and indoor scenes using internet images, achieving results comparable to interior designers as shown in experiments and a user study.
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.