English

StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions

Computer Vision and Pattern Recognition 2022-03-18 v2

Abstract

We apply style transfer on mesh reconstructions of indoor scenes. This enables VR applications like experiencing 3D environments painted in the style of a favorite artist. Style transfer typically operates on 2D images, making stylization of a mesh challenging. When optimized over a variety of poses, stylization patterns become stretched out and inconsistent in size. On the other hand, model-based 3D style transfer methods exist that allow stylization from a sparse set of images, but they require a network at inference time. To this end, we optimize an explicit texture for the reconstructed mesh of a scene and stylize it jointly from all available input images. Our depth- and angle-aware optimization leverages surface normal and depth data of the underlying mesh to create a uniform and consistent stylization for the whole scene. Our experiments show that our method creates sharp and detailed results for the complete scene without view-dependent artifacts. Through extensive ablation studies, we show that the proposed 3D awareness enables style transfer to be applied to the 3D domain of a mesh. Our method can be used to render a stylized mesh in real-time with traditional rendering pipelines.

Keywords

Cite

@article{arxiv.2112.01530,
  title  = {StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions},
  author = {Lukas Höllein and Justin Johnson and Matthias Nießner},
  journal= {arXiv preprint arXiv:2112.01530},
  year   = {2022}
}

Comments

Accepted to CVPR2022; project page: https://lukashoel.github.io/stylemesh/ ; video: https://www.youtube.com/watch?v=ZqgiTLcNcks ; code: https://github.com/lukasHoel/stylemesh

R2 v1 2026-06-24T08:02:15.966Z