I wonder if perceiving 3d shape from a 2d image has anything to do with manipulating 3d objects in visual memory.

The Todd’s reading took the above idea of properties of depth cues a little further. The main focus in this reading was on perception of depth cues. Fully understanding some of the concepts presented in his paper seems required in order to fully exploit the power of 3D or 2.5D design in visualization. For example the proper use of environmental constraints that can lead to accurate stimulation of a physical 3D shape etc. This seems especially true if one is to design custom 3D shapes.

Ware’s chapter tended to stay more true to using depth and perception cues to create the actual illusion of three-dimensional figures discussed in the Todd article. Some of the techniques discussed for using the thrird dimension as a visualization tool actually appear surprisingly effective. For instance, using subtle highlighting to enhance UI widgets is actually far more common than I’d realized. There are several instances of it to be found just simply looking at my browser window.

Additionally, his discussion of techniques such as motion parallax and atmospheric depth cues offered a chance to understand very commonly used techniques in three-dimensional modelling that the layman may not immediately recognize as illusion. According to Ware, such techniques can be integrated into current three-dimensional visualizations in order to improve the perceptual “friendliness” of these visualizations. However, Ware also notes that motion techniques have the power to make the stationary observer feel as if they are actually moving. This technique does logically seem like a useful tool in virtual reality simulations.

The most interesting part of the this chapter is the second half, where he discusses artificial spaces, and how they are beginning to become as natural to navigate as real physical spaces, even though they operate by a completely different set of rules. I wonder if learning a second set of rules for navigating an environment compromises our ability to understand and navigate the physical world?

Todd’s article addressed the fact that 3D shape perception isn’t inherently a correspondence in the mathematical sense, but maybe with the right set of constraints it could be. He points out that shading can be ambiguous under certain circumstances, and analyzing patterns is only effective when making specific assumptions that may not be true. He seems to focus mainly on curvature of an object, since it is an intrinsically defined attribute, and it is invariant to small rotations in most cases.

I am extremely surprised ( and doubtful ) to know the author’s finding that humans can detect both convex and concave shapes quite accurately.

Although shading is an important clue to understand the depth, it also produces ambiguities. The concept of “Stable” maps is something new to me, and although I knew the importance of curvature, ridge and valley, hatching etc could provide extremely powerful clues, I wasn’t aware that it is because of “Stable Data Structures”.

Image features are important and “T Junction” and “Cusp” locations provide important clues because of viewpoint-invariance. But other papers ( Malick etc)
gave better explanation than the present one.

It is interesting to note that local structures are sampled efficiently by human eyes are result is consistent in least square sense.

Lastly, I am extremely surprised to know the involvement of monkeys in understanding the human 3D perception capabilities.

Todd’s paper was hard to read. fMRI seems to contribute a lot in cognitive science.

The qualitative aspects of 3D object perception seems as if there is more room for interpretation and error. Perception of unfamiliar shapes could, therefore, be mistakenly identified as familiar objects by the observer, especially if other attributes besides shape (size, hue, orientation, etc.) are similar to known objects.

I’m beginning to grow tired of Ware’s chapters. His section on artificial interactive spaces came across as complete filler. His assertions are comparable to Tufte in the sense that there is often no reference to empirical research that supports his position. For a book on visualization and design, there seems to be a lot of low quality images and graphics that most people would consider clip art. I wouldn’t mind the examples as much if the written content didn’t come across as so groundlessly basic.

Chapter 5 of Ware’s textbook expands the 2.5D view to 3D. As the title of this chapter shows, the third dimension can be (but not necessary) either the spatial or the time dimension. The 2.5D discussion in previous chapters is mostly for static and non-interactive designs. The concept of affordances introduced by psychologist in 60s is useful in studying the visualization. Cognitive affordances are perceived possibilities for actions. To describe the third dimension, ten methods are listed in this chapter with features introduced. It is a quite useful reference for visualizing different types of data.

Todd provides a summary of the current understanding of our visual perception of 3D shape. How do we determine the 3D shape of an object by looking at a pattern of optical simulation, be it image shading, gradients of textures made up of dots or lines, or line drawings showing contours and curvature. It is possible for different 3D shapes to “create” the same pattern of optical simulation, yet, we seem to figure out what we are looking at even if the thing we are looking at has been distorted. We can do this as long at the distortions are within “limits.” We may be employing clues from the natural environment to figure out what we are actually looking at. Perception of 3D shapes remains relatively poorly understood, but survival seems to be the recurring theme in influencing how we see things.

Frankly, I found Todd’s paper to be unnecessarily dense and difficult to read, especially in comparison with Ware’s 5th chapter on Visual Space and Time.

Visual Space and Time by Ware
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Ware posits that scanning for information is essentially traversing space to get information into our heads. In that sense, it is conceptually the same as traveling physically over distance to see someone or something. From this perspective, he approaches the structure of 3D space.

The perceptual 3D space is made up of /up/, the /sideways/ and the /towards-away/ dimensions. Depth cues are bits of environmental clues that we use to judge distances in the /towards-away/ dimension. This dimension has inherently less information than the other two dimensions, since the latter are mapped on the retina. Hence, equating the /t-a/ dimension to half a dimension. Depth cues can be pictorial (occulusion, size and texture gradients, linear perspective, shadows, height on the picture plane, etc.) or non-pictorial (stereopsis, motion, focus, etc.).

Ware does a cost-benefit analysis of physically moving through space vs. “visualizing” 3D space, which can be useful in guiding our approach to visualization.

The take-away point is that depth is perceived in fundamentally different ways than other spatial dimensions. The visualization designer can utilize a combination of different cues to convey depth.

Thinking of design strategy implications, it can be inferred that 3D/2.5D is helpful in layering additional information, preferrably least cognitive load as possible. However, I think there may be also some benefits in more cognitive-heavy 3D perception as well if a specific visualization can benefit from immersive experience, such as photo-real exploration. For example, affordance could be a good concept to combine with interactivity, since it includes action by definition.

Todd’s article states that the “3D perceptual representation is primarily based on qualitative aspects of the structure itself” including occlusion contours or high curvature edges, and argues that ambiguities can be resolved by assigning better cues to the visual. It reminded me of the “spinning dancer illusion”(http://en.wikipedia.org/wiki/The_Spinning_Dancer), where the illusion is removed just by adding one small contour cue. As for design strategy, if we can use contours and edges to visualize the shape of 3D structures wisely, it will give us more freedom to use other encodings such as color and pattern for representing other data classes.