Name
Abstract | ||
|---|---|---|
Image-space line integral convolution (LIC) is a popular approach for visualizing surface vector fields due to its simplicity and high efficiency. To avoid inconsistencies or color blur during the user interactions in the image-space approach, some methods use surface parameterization or 3D volume texture for the effect of smooth transition, which often require expensive computational or memory cost. Furthermore, those methods cannot achieve consistent LIC results in both granularity and color distribution on different scales. This paper introduces a novel image-space LIC for surface flows that preserves the texture coherence during user interactions. To make the noise textures under different viewpoints coherent, we propose a simple texture mapping technique that is local, robust and effective. Meanwhile, our approach pre-computes a sequence of mipmap noise textures in a coarse-to-fine manner, leading to consistent transition when the model is zoomed. Prior to perform LIC in the image space, the mipmap noise textures are mapped onto each triangle with randomly assigned texture coordinates. Then, a standard image-space LIC based on the projected vector fields is performed to generate the flow texture. The proposed approach is simple and very suitable for GPU acceleration. Our implementation demonstrates consistent and highly efficient LIC visualization on a variety of datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/PacificVis.2012.6183584 | PacificVis |
Keywords | Field | DocType |
output-coherent image-space lic,novel image-space lic,volume texture,mipmap noise texture,texture coherence,efficient lic visualization,simple texture mapping technique,user interaction,surface flow visualization,flow texture,consistent lic result,noise texture,user interfaces,vector field,image texture,line integral convolution,data visualisation,flow visualization,texture mapping,smooth transition | Mipmap,Computer vision,Texture mapping,Data visualization,Computer science,Vector field,Image texture,Visualization,Artificial intelligence,Granularity,Line integral convolution | Conference |
ISSN | Citations | PageRank |
2165-8765 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Huang | 1 | 562 | 34.40 |
| wenjie pei | 2 | 43 | 8.10 |
| Chunfeng Wen | 3 | 0 | 0.34 |
| Guoning Chen | 4 | 320 | 23.72 |
| Wei Chen | 5 | 1193 | 92.00 |
| Hujun Bao | 6 | 2801 | 174.65 |