RESUMO
Relating small-scale structures to large-scale appearance is a key element in material appearance design. Bi-scale material design requires finding small-scale structures - meso-scale geometry and micro-scale BRDFs - that produce a desired large-scale appearance expressed as a macro-scale BRDF. The adjustment of small-scale geometry and reflectances to achieve a desired appearance can become a tedious trial-and-error process. We present a learning-based solution to fit a target macro-scale BRDF with a combination of a meso-scale geometry and micro-scale BRDF. We confront challenges in representation at both scales. At the large scale we need macro-scale BRDFs that are both compact and expressive. At the small scale we need diverse combinations of geometric patterns and potentially spatially varying micro-BRDFs. For large-scale macro-BRDFs, we propose a novel 2D subset of a tabular BRDF representation that well preserves important appearance features for learning. For small-scale details, we represent geometries and BRDFs in different categories with different physical parameters to define multiple independent continuous search spaces. To build the mapping between large-scale macro-BRDFs and small-scale details, we propose an end-to-end model that takes the subset BRDF as input and performs classification and parameter estimation on small-scale details to find an accurate reconstruction. Compared with other fitting methods, our learning-based solution provides higher reconstruction accuracy and covers a wider gamut of appearance.
RESUMO
A 3D modeling system with all-inclusive functionality is too demanding for a casual 3D modeler to learn. There has been a shift towards more approachable systems, with easy-to-learn, intuitive interfaces. However, most modeling systems still employ mouse and keyboard interfaces, despite the ubiquity of tablet devices and the benefits of multi-touch interfaces. We introduce an alternative 3D modeling and fabrication paradigm using developable surfaces, inspired by traditional papercrafting, and we implement it as a complete system designed for a multi-touch tablet, allowing a user to fabricate 3D scenes. We demonstrate the modeling and fabrication process of assembling complex 3D scenes from a collection of simpler models, in turn shaped through operations applied to virtual paper. Our fabrication method facilitates the assembly of the scene with real paper by automatically converting scenes into a series of cutouts with appropriately added fiducial markers and supporting structures. Our system assists users in creating occluded supporting structures to help maintain the spatial and rigid properties of a scene without compromising its aesthetic qualities. We demonstrate several 3D scenes modeled and fabricated in our system, and evaluate the faithfulness of our fabrications relative to their virtual counterparts and 3D-printed fabrications.
RESUMO
We propose a simple yet efficient steganalytic algorithm for watermarks embedded by two state-of-the-art 3D watermarking algorithms by Cho et al. The main observation is that while in a clean model the means/variances of Cho et al.'s normalized histogram bins are expected to follow a Gaussian distribution, in a marked model their distribution will be bimodal. The proposed algorithm estimates the number of bins through an exhaustive search and then the presence of a watermark is decided by a tailor made normality test or a t-test. We also propose a modification of Cho et al.'s watermarking algorithms with the watermark embedded by changing the histogram of the radial coordinates of the vertices. Rather than targeting a continuous statistics such as the mean or variance of the values in a bin, the proposed watermarking modifies a discrete statistic, which here is the height of the histogram bin, to achieve watermark embedding. Experimental results demonstrate that the modified algorithm offers not only better resistance against the steganalytic attack we developed, but also an improved robustness/capacity trade-off.
RESUMO
The BTF data structure was a breakthrough for appearance modeling in computer graphics. More research is needed though to make BTFs practical in rendering applications. We present the first systematic study of the effects of Approximate filtering on the appearance of BTFs, by exploring the spatial, angular and temporal domains over a varied set of stimuli. We perform our initial experiments on simple geometry and lighting, and verify our observations on more complex settings. We consider multi-dimensional filtering versus conventional mipmapping, and find that multi-dimensional filtering produces superior results. We examine the tradeoff between under- and oversampling, and find that different filtering strategies can be applied in each domain, while maintaining visual equivalence with respect to a ground truth. For example, we find that preserving contrast is more important in static than dynamic images, indicating greater levels of spatial filtering are possible for animations. We find that filtering can be performed more aggressively in the angular domain than in the spatial. Additionally, we find that high-level visual descriptors of the BTF are linked to the perceptual performance of pre-filtered approximations. In turn, some of these high-level descriptors correlate with low level statistics of the BTF. We show six different practical applications of applying our findings to improving filtering, rendering and compression strategies.
RESUMO
The past 10 years have seen profound changes in visualization algorithms, techniques, methodologies, and applications. These changes are forcing alterations to visualization courses. Unfortunately, outdated course content recommendations, together with profound changes in the underlying technology and methodology, are producing an unstable ground for educators at a time when visual representations are becoming increasingly important. To address this issue, educators held meetings or workshops at Siggraph 2011 and 2012 and a panel and workshop at Eurographics 2012. This article presents the insights gathered at these events.