89,195 research outputs found
Learning Material-Aware Local Descriptors for 3D Shapes
Material understanding is critical for design, geometric modeling, and
analysis of functional objects. We enable material-aware 3D shape analysis by
employing a projective convolutional neural network architecture to learn
material- aware descriptors from view-based representations of 3D points for
point-wise material classification or material- aware retrieval. Unfortunately,
only a small fraction of shapes in 3D repositories are labeled with physical
mate- rials, posing a challenge for learning methods. To address this
challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material
labels. We focus on furniture models which exhibit interesting structure and
material variabil- ity. In addition, we also contribute a high-quality expert-
labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We
further apply a mesh-aware con- ditional random field, which incorporates
rotational and reflective symmetries, to smooth our local material predic-
tions across neighboring surface patches. We demonstrate the effectiveness of
our learned descriptors for automatic texturing, material-aware retrieval, and
physical simulation. The dataset and code will be publicly available.Comment: 3DV 201
Enhancing urban analysis through lacunarity multiscale measurement
Urban spatial configurations in most part of the developing countries showparticular urban forms associated with the more informal urban development ofthese areas. Latin American cities are prime examples of this sort, butinvestigation of these urban forms using up to date computational and analyticaltechniques are still scarce. The purpose of this paper is to examine and extendthe methodology of multiscale analysis for urban spatial patterns evaluation. Weexplain and explore the use of Lacunarity based measurements to follow a lineof research that might make more use of new satellite imagery information inurban planning contexts. A set of binary classifications is performed at differentthresholds on selected neighbourhoods of a small Brazilian town. Theclassifications are appraised and lacunarity measurements are compared in faceof the different geographic referenced information for the same neighbourhoodareas. It was found that even with the simple image classification procedure, animportant amount of spatial configuration characteristics could be extracted withthe analytical procedure that, in turn, may be used in planning and other urbanstudies purposes
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