Automatic schelling points detection from meshes

Authors

Geng Chen, Inception Institute of Artificial Intelligence
Hang Dai, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Tao Zhou, Inception Institute of Artificial Intelligence
Jianbing Shen, ETH Zürich
Ling Shao, Inception Institute of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Visualization and Computer Graphics

Abstract

Mesh Schelling points explain how humans focus on specific regions of a 3D object. They have a large number of important applications in computer graphics and provide valuable information for perceptual psychology studies. However, detecting mesh Schelling points is time-consuming and expensive since the existing techniques are mostly based on participant observation studies. To overcome these limitations, we propose to employ powerful deep learning techniques to detect mesh Schelling points in an automatic manner, free from participant observation studies. Specifically, we utilize the mesh convolution and pooling operations to extract informative features from mesh objects, and then predict the 3D heat map of Schelling points in an end-to-end manner. In addition, we propose a Deep Schelling Network (DS-Net) to automatically detect the Schelling points, including a multi-scale fusion component and a novel region-specific loss function to improve our network for a better regression of heat maps. To the best of our knowledge, DS-Net is the first deep neural network for detecting Schelling points from 3D meshes. We evaluate DS-Net on a mesh Schelling point dataset obtained from participant observation studies. The experimental results demonstrate that DS-Net is capable of detecting mesh Schelling points effectively and outperforms various state-of-the-art mesh saliency methods and deep learning models, both qualitatively and quantitatively.

First Page

1

Last Page

1

DOI

10.1109/TVCG.2022.3144143

Publication Date

1-20-2022

Keywords

Deep learning, Deep Neural Network, Feature extraction, Geometric Deep Learning, Heat Map Regression, Heating systems, Image edge detection, Mesh Schelling Points, Point cloud compression, Shape, Three-dimensional displays

Comments

IR Deposit conditions:

• OA version (pathway a)
• Accepted version: No embargo
• When accepted for publication, set statement to accompany deposit (see policy)
• Must link to publisher version with DOI
• Publisher copyright and source must be acknowledged

Recommended Citation

G. Chen, H. Dai, T. Zhou, J. Shen and L. Shao, "Automatic schelling points detection from meshes," IEEE Transactions on Visualization and Computer Graphics, pp. 1-1, Jan. 2022, doi: 10.1109/TVCG.2022.3144143.