NNWarp: Neural Network-based Nonlinear Deformation
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-3859
- Type
- D - Journal article
- DOI
-
10.1109/TVCG.2018.2881451
- Title of journal
- IEEE Transactions on Visualization and Computer Graphics
- Article number
- -
- First page
- 1745
- Volume
- 26
- Issue
- 4
- ISSN
- 1077-2626
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- 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
-
6
- Research group(s)
-
D - CSE (Computational Science and Engineering)
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The significance of NNWarp is that it yields more accurate results than existing warping methods and can be used in applications requiring real time simulation of large scale 3D models.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
