Correlation-aware Encoder-Decoder with Adapters for SVBRDF Acquisition
Fig. 1.By modeling the correlation among input images with an encoder, together with an adapter-equipped decoder, our network achieves high-quality SVBRDF recovery on both isotropic and anisotropic (with roughness encoded in red and green channels) materials.Here we show re-rendered views for four materials under environment illumination.(Please use Adobe Acrobat and click the renderings to see the animation.)Capturing materials from the real world avoids laborious manual material authoring.However, recovering high-fidelity Spatially Varying Bidirectional Reflectance Distribution Function (SVBRDF) maps from a few captured images is challenging due to its ill-posed nature.Existing approaches have made extensive efforts to alleviate this ambiguity issue by leveraging generative models with latent space optimization or extracting features with variant encoder-decoders.Albeit the rendered images at input views can match input images, the problematic decomposition among maps leads to significant differences when rendered under novel views/lighting.We observe that for human eyes, besides individual images, the correlation (or the highlights variation) among input images also serves as an important hint to recognize * Contribute equally.
Authors
- Beibei Wang (ORCID: https://orcid.org/0000-0001-8943-8364)
- Лей Ма (ORCID: https://orcid.org/0000-0001-6024-3854)
- Hanxiao Sun (ORCID: https://orcid.org/0009-0006-4108-517X)
- Jian Yang (ORCID: https://orcid.org/0000-0003-4800-832X)
- Di Luo
Institutions
- Peking University (CN)
- Nankai University (CN)
- Suzhou University of Science and Technology (CN)
- Nanjing University (CN)
Publication Details
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1145/3680528.3687594
- Citations
- 4
- Primary Topic
- Computer Graphics and Visualization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.53
Funders
- National Natural Science Foundation of China
- Nankai University
- National Science and Technology Major Project