Name
Abstract | ||
|---|---|---|
In this work, a novel 6DoF mesh saliency database is developed which provides both the subject’s 6DoF data and eye-movement data. Different from traditional databases, subjects in the experiment are allowed to move freely to view 3D meshes in the virtual reality environment. Based on the database, we first analyze the inter-observer variation and the influence of viewing direction toward subject’s visual attention, then we provide investigations about the subject’s visual attention bias and head movement during observation. As traditional 3D mesh saliency detection algorithms do not taking the subject’s head movement into consideration, we further propose a 6DoF mesh saliency detection algorithm based on the uniqueness measure and the bias preference. To evaluate the proposed approach, we design an evaluation metric accordingly which takes the 6DoF information into consideration, and extend some state-of-the-art 3D saliency detection methods to make comparisons. The experimental results demonstrate the superior performance of our approach for 6DoF mesh saliency detection, in addition to providing benchmarks for the presented 6DoF mesh saliency database. The database and proposed method will be made publicly available for research purposes. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.neucom.2022.06.088 | Neurocomputing |
Keywords | DocType | Volume |
6DoF,Visual attention behavior,6DoF mesh saliency detection | Journal | 502 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoying Ding | 1 | 2 | 1.10 |
| Zhenzhong Chen | 2 | 1244 | 101.41 |