SIGGRAPH Asia 2018
Transactions on Graphics (TOG) Special Issue Cover

PhotoShape: Photorealistic Materials for Large-Scale Shape Collections

Abstract

Existing online 3D shape repositories contain thousands of 3D models but lack photorealistic appearance. We present an approach to automatically assign high-quality, realistic appearance models to large scale 3D shape collections. The key idea is to jointly leverage three types of online data – shape collections, material collections, and photo collections, using the photos as reference to guide assignment of materials to shapes. By generating a large number of synthetic renderings, we train a convolutional neural network to classify materials in real photos, and employ 3D-2D alignment techniques to transfer materials to different parts of each shape model. Our system produces photorealistic, relightable, 3D shapes (PhotoShapes).


Fully automatic texturing of 3D shapes with rich SV-BRDF reflectance models.

Code and Data

Coming Soon

Bibtex

@article{photoshape2018,
 author = {Park, Keunhong and Rematas, Konstantinos and Seitz, Steven M. and Farhadi, Ali},
 title = {PhotoShape: Photorealistic Materials for Large-Scale Shape Collections},
 journal = {ACM Trans. Graph.},
 issue_date = {November 2018},
 volume = {37},
 number = {6},
 month = nov,
 year = {2018},
 articleno = {192},
}

Acknowledgements

This work was supported by the Samsung Scholarship, the Allen Institute for Artificial Intelligence, Intel, Google, and the National Science Foundation (IIS1538618).