Abstract
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 surface 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 a Markov Random Field (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.
Original language | English |
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Article number | 8567954 |
Pages (from-to) | 2204-2218 |
Number of pages | 15 |
Journal | IEEE Transactions on Visualization and Computer Graphics |
Volume | 26 |
Issue number | 6 |
Early online date | 7 Dec 2018 |
DOIs | |
Publication status | Published - 1 Jun 2020 |
Keywords
- 3D mesh
- Markov random field
- distinction
- neural network
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YONGHUAI LIU
- Computer Science - Professor of Computer Games & Graphics
- Health Research Institute
Person: Research institute member, Academic