Regional importance detection of 3D mesh via fusion of local color difference and curvature entropy

INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING(2022)

引用 0|浏览0
暂无评分
摘要
Predicting relative visual salient regions on three-dimensional (3D) meshes benefits many computer graphics applications. Most computation models for mesh saliency focus on geometrical information alone. Nevertheless, the ignored texture, lighting and material also provide more detailed appearance information, especially in the context of static scene rendering. In this paper, we propose a mesh saliency detection algorithm considering both geometrical and colorimetric information to address this challenge. Our model first computes the local curvature entropy at multi-scale to capture the geometrical details. Second, a set of images are projected onto the screen at several viewpoints with specified material and lighting model. Potentially salient regions on the rendered images are detected by fusion of multiple color difference maps measured with an approximated multi-scale Laplacian of Gaussian filter. A Gaussian distribution-based central bias model is applied to the image saliency map to emphasize the global rarity of salient regions. Third, the saliency maps of rendered images are projected back to the 3D mesh via the ray casting method. In the end, both saliency maps are combined linearly as the saliency map of 3D mesh. Experiment on the human fixation database demonstrates the performance of our method compared to the classic methods in terms of linear correlation coefficient and AUC.
更多
查看译文
关键词
Mesh saliency, colorimetric, color difference, Laplacian of Gaussian
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要