Orientation Fields of a Rotating “Blobby” Object
In research I will be presenting in a few days at VSS (the Vision Sciences Society annual meeting), I will be demonstrating how we may use orientation flow fields of texture and shading when making perceptual judgments of 3D shape structure (see Fleming, Holtmann-Rice, & Bülthoff, 2011 for additional information). Since I find visualizations fun, I decided to use some spare CPU cycles overnight to visualize the orientation fields of a rotating blobby object.
The object on the left in the above video is a textured and shaded object with a small amount of specular reflection (lit using the Debvec Funston Beach at Sunset light probe). On the right, I’m illustrating the dominant orientations in the image, across the surface of the object.
Click through for some more visualizations.
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Visual perception of the physical stability of asymmetric three-dimensional objects
I recently published an article in the Journal of Vision with my PhD advisor, Manish Singh, and my current Postdoctoral advisor, Roland W. Fleming:
Cholewiak, S. A., Fleming, R. W., & Singh, M. (2013). Visual perception of the physical stability of asymmetric three-dimensional objects. Journal of Vision, 13(4), 1–13. doi: 10.1167/13.4.12
Here’s the abstract:
Visual estimation of object stability is an ecologically important judgment that allows observers to predict the physical behavior of objects. A natural method that has been used in previous work to measure perceived object stability is the estimation of perceived “critical angle”—the angle at which an object appears equally likely to fall over versus return to its upright stable position. For an asymmetric object, however, the critical angle is not a single value, but varies with the direction in which the object is tilted. The current study addressed two questions: (a) Can observers reliably track the change in critical angle as a function of tilt direction? (b) How do they visually estimate the overall stability of an object, given the different critical angles in various directions? To address these questions, we employed two experimental tasks using simple asymmetric 3D objects (skewed conical frustums): settings of critical angle in different directions relative to the intrinsic skew of the 3D object (Experiment 1), and stability matching across 3D objects with different shapes (Experiments 2 and 3). Our results showed that (a) observers can perceptually track the varying critical angle in different directions quite well; and (b) their estimates of overall object stability are strongly biased toward the minimum critical angle (i.e., the critical angle in the least stable direction). Moreover, the fact that observers can reliably match perceived object stability across 3D objects with different shapes suggests that perceived stability is likely to be represented along a single dimension.
Want to cite us? Click through for the BibTeX source.
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Effect of Environment Map Blur on Perceived Surface Properties
Here are a couple of quick demos, illustrating how blurring a cubic environmental map can lead to a change in the perceived roughness of the surface of 3D rendered objects.
I created a series of HDR cube maps using NVIDIA’s CubeMapGen (currently hosted on Google Code). Starting with the Debevec light probes, I applied a Gaussian blur with increasing kernel size (10°, 20°, 30°, 40°, and 50°), creating 6 cube maps (one for each blur). In the videos, the cube maps have increasing blur from left-to-right, top-to-bottom. Note that I did not tone-map or account for changes in overall exposure (so the specular reflections can appear blown-out, especially for the higher blurs). After the break, you can see the effect using different light probes (and different shapes).
Misperceived axis of rotation for objects with specular reflections
Katja Dörschner visited JLU last week and talked about her work investigating structure from motion with specular reflections and textures (see more info in her recent paper: Doerschner, Fleming, Yilmaz, Schrater, Hartung, & Kersten, 2011). She showed an interesting situation where the axis of rotation of a 3D teapot was misperceived due to the motion of the specular reflections on the surface of the teapot (see: Yilmaz, Kucukoglu, Fleming, & Doerschner, 2011), an effect first demonstrated by Hartung and Kersten (2002).
Using the OpenGL/Psychtoolbox framework I have previously described, I replicated this interesting effect. When you play the following movie, a 3D sphere (with sinusoidal perturbations) is rotated. Note the axis of perceived rotation when the object has specular reflections (1st half of the movie) and when the environment map is “painted” onto the surface (2nd half of the movie).
The physical motion of the object is the same in both cases — the object rotates around the vertical axis. When the object only has specular reflections, it appears to rotate around an oblique 45° axis, but when textured it rotates around the vertical axis. After the break, I show similar effects when the object is rotated around the horizontal axis, 45° axis, and when the spatial frequency of the perturbation is manipulated.
Blobby Eye Candy
Using the building blocks previously described (see 1, 2, 3, & 4) along with some other creative coding, I have been able to generate some nice stimuli. Here is an example of a random shape being spun along the 3 axes while its surface properties (texture, shading, and specular reflections) are manipulated:
2 8 more videos (with different shapes and illumination conditions) after the break.
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3D “Potato” Generation using Sinusoidal Pertubations
Generating unique 3D stimuli can be an art-form.
In order to generate “organic” stimuli with smooth undulations, I needed to systematically manipulate the surface meshes of 3D spheres to create smooth peaks and valleys.
3D MATLAB noise – effect of changing Gaussian convolution kernel size
To illustrate the effect of changing the Gaussian convolution kernel size, I generated a series of 64x64x64 3D noise texture arrays using the code from my 3D MATLAB noise (continued) post:
i = 64;
for k = 1:2:31
tic;
noise{i,k} = noise3Dwrap(i,k);
imwrite(reshape(noise{i,k},[i i 1 i]),...
['noise3Dwrap_s' num2str(i) 'k' num2str(k) '.gif'],...
'LoopCount',Inf,'DelayTime',0.10);
timeToProcess{i,k} = toc;
save('noise3Dwrap64_k1_31.mat')
end
After the break, see how increasing the size of the convolution kernel affects the quality of the 3D noise.
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3D MATLAB noise (continued)
So, I have figured out a way to generate some nice, 3D noise that wraps in all 3 dimensions offline, which allows it to be read into a 3D texture in OpenGL. Here is an example video of slices of the 3D texture:
Continue after the break to see how this 3D noise was generated.
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