LIME: Live Intrinsic Material Estimation
- Submitting institution
-
The University of Bath
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 188637010
- Type
- E - Conference contribution
- DOI
-
10.1109/CVPR.2018.00661
- Title of conference / published proceedings
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- First page
- 6315
- Volume
- -
- Issue
- -
- ISSN
- 2575-7075
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
http://rll.berkeley.edu/bigbird/
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is published as a Spotlight Paper at CVPR, the leading conference in computer vision and fifth-ranked publication venue overall according to Google Scholar in 2015–2019. The acceptance rate for this track was 6.6% (224 papers out of 3359 submissions). This work is the first end-to-end approach for real-time material estimation for general object shapes with uniform material that only requires a single colour image as input. This enables important mixed-reality applications, such as seamless illumination-consistent integration of virtual objects into real-world scenes, and virtual material cloning.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -