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Toni Saarela; ShapeToolbox: Creating 3D models for vision research. Journal of Vision 2018;18(10):229. doi: https://doi.org/10.1167/18.10.229.
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© ARVO (1962-2015); The Authors (2016-present)
Many studies on visual perception and cognition use physically realistic, rendered images of 3D computer graphics models as stimuli. Precise parametric control is often needed over variations in properties such as shape and surface corrugation. Such parametric control over stimulus properties is crucial in many experiments on material perception, object recognition, shape adaptation, and shape memory. It would thus be desirable to have a software tool that produces 3D shapes with the needed level of parametric control, is easy to use, can be extended and modified by the user if needed, and is free. We introduce the ShapeToolbox, a collection of tools for creating 3D models of various shapes. This toolbox is free, open-source software that runs on Matlab and Octave; on GNU/Linux, Mac OS, and Windows platforms. The toolbox provides a handful of "base shapes" (spheres, disks, planes, tori, surfaces-of-revolution, and so forth) that can then be perturbed and modified in various ways. The options for 3D shape perturbation include sinusoidal components, filtered noise, Gaussian bumps or dents, and user-provided custom functions, matrices, or images. All shape and perturbation parameters are given numerically (instead of, say, deforming the shape using a mouse). In addition to enabling precise control over the stimulus, this also makes reporting and replicating the stimuli and experiments easier. Different kinds of perturbation can be freely combined in a given model, and ShapeToolbox also supports the blending of two shapes. The main strength of the toolbox lies in its use from code, but simple graphical user interfaces are also included for 3D model design and blending. The models can be saved in Wavefront obj format for importing to rendering programs or OpenGL applications. To illustrate the use and usefulness of the toolbox, we report data from example psychophysical experiments on shape recognition and surface material discrimination.
Meeting abstract presented at VSS 2018
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