Web: http://arxiv.org/abs/2206.08423

June 20, 2022, 1:13 a.m. | Rui Zhu, Zhengqin Li, Janarbek Matai, Fatih Porikli, Manmohan Chandraker

cs.CV updates on arXiv.org arxiv.org

Indoor scenes exhibit significant appearance variations due to myriad
interactions between arbitrarily diverse object shapes, spatially-changing
materials, and complex lighting. Shadows, highlights, and inter-reflections
caused by visible and invisible light sources require reasoning about
long-range interactions for inverse rendering, which seeks to recover the
components of image formation, namely, shape, material, and lighting. In this
work, our intuition is that the long-range attention learned by transformer
architectures is ideally suited to solve longstanding challenges in
single-image inverse rendering. We demonstrate …

arxiv cv image inverse rendering transformers vision

More from arxiv.org / cs.CV updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY