Browsing by Subject "Computer graphics"

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    A Computational Framework for Predicting Appearance Differences
    (2018-07) Ludwig, Michael
    Quantifying the perceived difference in appearance between two surfaces is an important industrial problem that currently is solved through visual inspection. The field of design has always employed trained experts to manually compare appearances to verify manufacturing quality or match design intent. More recently, the advancement of 3D printing is being held back by an inability to evaluate appearance tolerances. Much like color science greatly accelerated the design of conventional printers, a computational solution to the appearance difference problem would aid the development of advanced 3D printing technology. Past research has produced analytical expressions for restricted versions of the problem by focusing on a single attribute like color or by requiring homogeneous materials. But the prediction of spatially-varying appearance differences is a far more difficult problem because the domain is highly multi-dimensional. This dissertation develops a computational framework for solving the general form of the appearance comparison problem. To begin, a method-of-adjustment task is used to measure the effects of surface structure on the overall perceived brightness of a material. In the case considered, the spatial variations of an appearance are limited to shading and highlights produced by height changes across its surface. All stimuli are rendered using computer graphics techniques in order to be viewed virtually, thus increasing the number of appearances evaluated per subject. Results suggest that an image-space model of brightness is an accurate approximation, justifying the later image-based models that address more general appearance evaluations. Next, a visual search study is performed to measure the perceived uniformity of 3D printed materials. This study creates a large dataset of realistic materials by using state-of-the-art material scanners to digitize numerous tiles 3D printed with spatially- varying patterns in height, color, and shininess. After scanning, additional appearances are created by modifying the reflectance descriptions of the tiles to produce variations that cannot yet be physically manufactured with the same level of control. The visual search task is shown to efficiently measure changes in appearance uniformity resulting from these modifications. A follow-up experiment augments the collected uniformity measurements from the visual search study. A forced-choice task measures the rate of change between two appearances by interpolating along curves defined in the high-dimensional appearance space. Repeated comparisons are controlled by a Bayesian process to efficiently find the just noticeable difference thresholds between appearances. Gradients reconstructed from the measured thresholds are used to estimate perceived distances between very similar appearances, something hard to measure directly with human subjects. A neural network model is then trained to accurately predict uniformity from features extracted from the non-uniform appearance and target uniform appearance images. Finally, the computational framework for predicting general appearance differences is fully developed. Relying on the previously generated 3D printed appearances, a crowd-sourced ranking task is used to simultaneously measure the relative similarities of multiple stimuli against a reference appearance. Crowd-sourcing the perceptual data collection allows the many complex interactions between bumpiness, color, glossiness, and pattern to be evaluated efficiently. Generalized non-metric multidimensional scaling is used to estimate a metric embedding that respects the collected appearance rankings. The embedding is sampled and used to train a deep convolutional neural network to predict the perceived distance between two appearance images. While the learned model and experiments focus on 3D printed materials, the presented approaches can apply to arbitrary material classes. The success of this computational approach creates a promising path for future work in quantifying appearance differences.
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    Efficient and Robust ADMM Methods for Dynamics and Geometry Optimization
    (2022-01) Brown, George
    We present novel ADMM-based methods for efficiently solving problems in a variety of applications in computer graphics. First, in the domain of physics-based animation we propose new techniques for simulating elastic bodies subject to dissipative forces. Second, in the field of geometry optimization we introduce a new algorithm for quasi-static deformation and surface parameterization. In each of these applications our proposed methods robustly converge to accurate solutions, and do so faster than existing algorithms. We achieve this by using key insights to address and overcome many limitations of standard solvers. Here we highlight the features of our two new algorithms for the aforementioned problems. Then we summarize our preliminary investigations into new techniques we designed in pursuit of faster and more reliable convergence in parameterization problems. Our first method is one for incorporating dissipative forces into optimization-based time integration schemes, which hitherto have been applied almost exclusively to systems with only conservative forces. We represent such forces using dissipation functions that may be nonlinear in both positions and velocities, enabling us to model a range of dissipative effects including Coulomb friction, Rayleigh damping, and power-law dissipation. To improve accuracy and minimize artificial damping, we provide an optimization-based version of the second-order accurate TR-BDF2 integrator. Finally, we present a method for modifying arbitrary dissipation functions to conserve linear and angular momentum, allowing us to eliminate the artificial angular momentum loss caused by Rayleigh damping. Our second method is designed to efficiently solve geometry optimization problems. We observe that in this domain existing local-global solvers such as ADMM struggle to resolve large rotations such as bending and twisting modes, and large distortions in the presence of barrier energies. We propose two improvements to address these challenges. First, we introduce a novel local-global splitting based on the polar decomposition that separates the geometric nonlinearity of rotations from the material nonlinearity of the deformation energy. The resulting ADMM-based algorithm is a combination of an L-BFGS solve in the global step and proximal updates of element stretches in the local step. We also introduce a novel method for dynamic reweighting that is used to adjust element weights at runtime for improved convergence. With both improved rotation handling and element weighting, our WRAPD algorithm is considerably faster than state-of-the-art approaches for quasi-static simulations. WRAPD is also much faster at making early progress in parameterization problems, making it valuable as an initializer to jump-start second-order algorithms. Finally, we investigate two possible extensions to WRAPD for accelerated convergence in parameterization problems. The first extension, P-WRAPD, leverages progressive reference shape updates similar to Liu et al. [2018] to bound distortion. We show that this yields minor improvements in a subset of examples. Our second extension, N-WRAPD, uses a non-scalar weighting scheme that independently assigns unique weights for every mode of deformation. This method shows promising preliminary results. Although slightly less stable, N-WRAPD generally converges significantly faster at a rate comparable to second-order algorithms.
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    Oral history interview with Arnold Spielberg
    (Charles Babbage Institute, 1987-06-23) Spielberg, Arnold
    Spielberg, an electronics engineer and manager in Product Technology Operations for Unisys, discusses product development in the computer industry. He describes his work with RCA and General Electric Computer Dept. in the 1950s; IBM, Scientific Data Systems, and Electronic Arrays in the 1960s; and his work with Burroughs (and later Unisys) after 1973. Subjects discussed include: point-of-sale equipment; product development and marketing; GE 225; IBM computers; Burroughs computers; Scientific Data System's SIGMA series; and GP2000 (a cooperative graphics product of Burroughs and Superset).