A Human-Perceived Softness Measure of Virtual 3D Objects
- Submitting institution
-
Liverpool Hope University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- KD34C
- Type
- D - Journal article
- DOI
-
10.1145/3193107
- Title of journal
- ACM Transactions on Applied Perception
- Article number
- -
- First page
- 1-18
- Volume
- 15
- Issue
- 3
- ISSN
- 1544-3965
- Open access status
- Compliant
- Month of publication
- June
- 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
- Yes
- Number of additional authors
-
3
- Research group(s)
-
S - Spatial Computing and Robotics (SC&R)
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In this work, we introduce the problem of computing a human-perceived softness measure for virtual 3D objects. An initial version of this work was presented at Proceedings of the ACM Symposium on Applied Perception, 2016. This work demonstrates the use of crowdsourcing and machine learning to build a measure of human-perceived softness of virtual 3D objects. The learned measure of softness finds applications in the areas of 3D fabrication and exploration of large 3D shape repositories. We also release a dataset of human perception of shape elasticity useful to the research community in this area.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
