# A Data-driven Approach for Furniture and Indoor Scene Colorization

arXiv: https://arxiv.org/abs/1702.08680 · HTML: https://mlsanity.com/paper/1702.08680/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2017.

## Built on by (most cited first, 3 of 3)

- [Patch-based 3D Natural Scene Generation from a Single Example](https://mlsanity.com/paper/2304.12670/) (2023)
- [Photo-to-Shape Material Transfer for Diverse Structures](https://mlsanity.com/paper/2205.04018/) (2022)
- [Realistic Image Synthesis with Configurable 3D Scene Layouts](https://mlsanity.com/paper/2108.10031/) (2021)

## Abstract (arXiv)

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.