Sparse Metric-Based Mesh Saliency

Shanfeng Hu, Xiaohui Liang, Hubert P. H. Shum, Frederick W. B. Li and Nauman Aslam
Neurocomputing, 2020

 Impact Factor: 6.0

Sparse Metric-Based Mesh Saliency


In this paper, we propose an accurate and robust approach to salient region detection for 3D polygonal surface meshes. The salient regions of a mesh are those that geometrically stand out from their contexts and therefore are semantically important for geometry processing and shape analysis. However, a suitable definition of region contexts for saliency detection remains elusive in the field, and the previous methods fail to produce saliency maps that agree well with human annotations. We address these issues by computing saliency in a global manner and enforcing sparsity for more accurate saliency detection. Specifically, we represent the geometry of a mesh using a metric that globally en- codes the shape distances between every pair of local regions. We then propose a sparsity-enforcing rarity optimization problem, solving which allows us to obtain a compact set of salient regions globally distinct from each other. We build a perceptually motivated 3D eye fixation dataset and use a large-scale Schelling saliency dataset for extensive benchmarking of saliency detection methods. The results show that our computed saliency maps are closer to the ground-truth. To showcase the usefulness of our saliency maps for geometry processing, we apply them to feature point localization and achieve higher accuracy compared to established feature detectors.





 author={Hu, Shanfeng and Liang, Xiaohui and Shum, Hubert P. H. and Li, Frederick W. B. and Aslam, Nauman},
 title={Sparse Metric-Based Mesh Saliency},


AU  - Hu, Shanfeng
AU  - Liang, Xiaohui
AU  - Shum, Hubert P. H.
AU  - Li, Frederick W. B.
AU  - Aslam, Nauman
T2  - Neurocomputing
TI  - Sparse Metric-Based Mesh Saliency
PY  - 2020
VL  - 400
SP  - 11
EP  - 23
DO  - 10.1016/j.neucom.2020.02.106
SN  - 0925-2312
PB  - Elsevier
ER  - 

Plain Text

Shanfeng Hu, Xiaohui Liang, Hubert P. H. Shum, Frederick W. B. Li and Nauman Aslam, "Sparse Metric-Based Mesh Saliency," Neurocomputing, vol. 400, pp. 11-23, Elsevier, 2020.

Supporting Grants

The Royal Society
Modelling Human Motion for Synthesis and Recognition with Deep Learning on Surface Features
Royal Society International Exchanges (Ref: IES\R2\181024): £12,000, Principal Investigator ()
Received from The Royal Society, UK, 2019-2022
Project Page
Erasmus Mundus
Sustainable Green Economies through Learning, Innovation, Networking and Knowledge Exchange (gLink)
Erasmus Mundus Action 2 Programme (Ref: 2014-0861/001-001): €3.03 million, Co-Investigator, Northumbria University Funding Management Leader (PI: Prof. Nauman Aslam)
Received from Erasmus Mundus, 2015-2018
Project Page

Similar Research

Shanfeng Hu, Hubert P. H. Shum, Nauman Aslam, Frederick W. B. Li and Xiaohui Liang, "A Unified Deep Metric Representation for Mesh Saliency Detection and Non-Rigid Shape Matching", IEEE Transactions on Multimedia (TMM), 2020
Shanfeng Hu, Hubert P. H. Shum and Antonio Mucherino, "DSPP: Deep Shape and Pose Priors of Humans", Proceedings of the 2019 ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG), 2019
Ziyi Chang, George Alex Koulieris and Hubert P. H. Shum, "3D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit Models", Proceedings of the 2022 ACM Symposium on Virtual Reality Software and Technology (VRST), 2022
Jingtian Zhang, Hubert P. H. Shum, Kevin D. McCay and Edmond S. L. Ho, "Prior-Less 3D Human Shape Reconstruction with an Earth Mover's Distance Informed CNN", Proceedings of the 2019 ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG) Posters, 2019



Last updated on 14 April 2024
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