Bas-Relief Modeling with Detail Preservation and Local Significance Enhancement

IEEE Access(2020)

引用 4|浏览50
暂无评分
摘要
Bas-relief, usually attached to a certain plane, is a semi-stereoscopic sculpture that is suitable for decoration of various environments. Since the rise of 3D printing technology, digital bas-relief modeling is greeted with an avalanche of publicity. In this work, for the detail loss caused by depth compression, we propose a bas-relief modeling method based on the idea of feature separation and migration, which can not only reduce the overall height, but also preserve the original details well. Different from the Weighted Least Squares filter with edge-preserving property, the result of which is affected by gradient changes in the input image, we propose the Full Least Squares filter that allows each region of the input image to receive the same level of smoothing. We first separate the features of bas-relief through the Full Least Squares filter. The details and edges are stripped out together and then combined with the deeply compressed structures to generate a flat bas-relief, in which the edge sinking phenomenon is subsequently optimized to ensure a natural transition between the content and the background. More than that, the watershed algorithm is used to segment the height map of bas-relief, and local detail enhancement or local depth constraint is implemented through mask operation to improve the local feature significance of bas-relief. Experiments show that our method effectively generates the bas-relief with strong flatness and well-preserved details. What & x2019;s more, the global and local adjustments of features produce rich and diverse planarization effects, which provides more choices for diverse designs of digital bas-relief.
更多
查看译文
关键词
Three-dimensional displays,Solid modeling,Image reconstruction,Image edge detection,Image coding,Shape,Two dimensional displays,Bas-relief modeling,depth compression,detail preservation,feature separation,local significance enhancement
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