Conveying Shape: Lines
From CS294-10 Visualization Sp11
Lecture on Apr 20, 2011
- Automatic illustration of 3D geometric models: Lines. Dooley and Cohen. (acm)
- Line Direction Matters. Girshick et al. (pdf)
- Suggestive contours. DeCarlo et al. (html)
- Illustrating smooth surfaces. Hertzmann and Zorin. (html)
- Automatic illustration of 3D geometric models: Surfaces. Dooley and Cohen. (pdf)
- Speed of Perception as a Function of Mode of Representation. Ryan and Schwartz. (jstor)
- Assessing the Effect of Non-Photerealistic Rendered images in CAD. Schumann et al. (html)
Saung Li - Apr 20, 2011 06:08:58 pm
The Girshick et al. paper provides a compelling argument for the use of principle directions to better convey the shape of an object. The direction of lines does indeed have an affect on our perception of shape: figures 2-6 provide illustrative examples of why using principle direction vector fields are more effective that other vector fields such as random and uniform ones. Adding silhouette lines further improves the quality of the image, as shown in figure 15, and it would be interest to see shading and variable length lines to improve the image even more. For some of these images, I don't see what we gain from representing objects using vector fields. For example, in the horse figure I feel like I can better understand the shape of the horse from a visualization such as a cartoon version of it that's colored in with its fur flowing in the direction of the vector field. From this I can still understand the shape of the horse and it will look better aesthetically and more realistic.
Michael Hsueh - Apr 22, 2011 12:44:30 am
I agree with Saung's assessment about the effectiveness of lines of principal direction. It was not initially obvious that they would be better than random vector fields, as there is a tendency to suspect that we might omit important information by focusing entirely on principal directions. Random vector fields almost seem as if they could provide more "coverage" of the general shape of objects due to their random nature. Judging from the results shown in the paper, this is simply not true, as more information comes from the varying density of lines shown (what amounts to some sort of pseudo-shading) than the shapes of the random lines themselves. Anyway, one of the key features of using principal directions is their geometric invariance, allowing animation of drawings without the distraction of view dependent details such as shading. By introducing view-dependent variables such as variable lighting or shading that are either guided or combined with principal direction vector lines, we can get a very nice representation of almost any shape. These additional details might help in some ways with dealing with difficult regions of opposing force or undefined principal direction.
Matthew Can - Apr 22, 2011 03:16:39 am
The problem of deciding which lines best convey shape is an interesting one. The work on suggestive contours is a solid contribution toward addressing this. But what I really would have liked to see is a formal user evaluation that compares this to other methods such as lines of principal curvature. The paper's brief, informal discussion doesn't satisfy me. But perhaps the reason they left out a formal evaluation has more to do with the expectations for graphics papers (as opposed to visualization or HCI) than anything else. In any case, one thing that caught my attention is their algorithm for computing suggestive contours from images. Unfortunately, they only showed results on images that were rendered from triangular mesh objects. I wonder if the algorithm would work on images captured with a digital camera.