Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Distinction of 3D objects and scenes via classification network and Markov random field

Song, Ran, Liu, Yonghuai and Rosin, Paul L. ORCID: 2020. Distinction of 3D objects and scenes via classification network and Markov random field. IEEE Transactions on Visualization and Computer Graphics 26 (6) 10.1109/TVCG.2018.2885750

[thumbnail of distinction-TVCG-postprint.pdf]
PDF - Accepted Post-Print Version
Download (12MB) | Preview


An importance measure of 3D objects inspired by human perception has a range of applications since people want computers to behave like humans in many tasks. This paper revisits a well-defined measure, distinction of 3D mesh, which indicates how important a region of a mesh is with respect to classification. We develop a method to compute it based on a classification network and an MRF. The classification network learns view-based distinction by handling multiple views of a 3D object. Using a classification network has an advantage of avoiding the training data problem which has become a major obstacle of applying deep learning to 3D object understanding tasks. The MRF estimates the parameters of a linear model for combining the view-based distinction maps. The experiments using several publicly accessible datasets show that the distinctive regions detected by our method are not just significantly different from those detected by methods based on handcrafted features, but more consistent with human perception. We also compare it with other perceptual measures and quantitatively evaluate its performance in the context of two applications. Furthermore, due to the view-based nature of our method, we are able to easily extend mesh distinction to 3D scenes containing multiple objects.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1077-2626
Date of First Compliant Deposit: 18 June 2019
Date of Acceptance: 3 December 2018
Last Modified: 13 Nov 2023 08:22

Citation Data

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics