一种数据驱动的家具和室内场景彩色化方法
A Data-driven Approach for Furniture and Indoor Scene Colorization
摘要：
我们提出了一种数据驱动的方法，通过利用互联网上的室内图像对3D家具模型和室内场景进行彩色化。我们的方法是能够着色的家具自动根据一个例子的图像。其核心是学习图像引导的网格分割，根据图像对象将模型分割成不同的部分。给定室内场景，该系统支持示例着色，并且具有推荐与用户期望的颜色主题一致的着色方案的能力。后者是通过将问题制定为马尔可夫随机场模型来实现的，该模型将用户输入作为附加约束。我们为社区贡献了一个分层组织的图像模型数据库，每个图像和相应的模型之间的部分级别的对应关系。我们的实验和用户研究表明，我们的系统产生的感知令人信服的结果相比，室内设计师所产生的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
