-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Li_2023_CVPR, author = {Li, Chenhao and Ngo, Trung Thanh and Nagahara, Hajime}, title = {Inverse Rendering of Translucent Objects Using Physical and Neural Renderers}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {12510-12520} }
Inverse Rendering of Translucent Objects Using Physical and Neural Renderers
Abstract
In this work, we propose an inverse rendering model that estimates 3D shape, spatially-varying reflectance, homogeneous subsurface scattering parameters, and an environment illumination jointly from only a pair of captured images of a translucent object. In order to solve the ambiguity problem of inverse rendering, we use a physically-based renderer and a neural renderer for scene reconstruction and material editing. Because two renderers are differentiable, we can compute a reconstruction loss to assist parameter estimation. To enhance the supervision of the proposed neural renderer, we also propose an augmented loss. In addition, we use a flash and no-flash image pair as the input. To supervise the training, we constructed a large-scale synthetic dataset of translucent objects, which consists of 117K scenes. Qualitative and quantitative results on both synthetic and real-world datasets demonstrated the effectiveness of the proposed model.
Related Material