A non-linear shape abstraction technique

Brown, Gary P.; Forte, Peter; Malyan, R.; Barnwell, Peter

doi:10.1007/3-540-57233-3_30

Cited by 3 publications

(2 citation statements)

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“…Other avenues explored for abstraction include rule-based simplification [Brown et al 1993], tracking a symbolic representation while the user constructs a parametric model [Falcidieno and Spagnuolo 1998], or topology-based inference [Biasotti et al 2002]. While the general shape analysis problem is difficult even for manifold, connected meshes, we show that for man-made objects, due to their inherent regularity and structure, creating effective abstraction, even from polygon soups, is possible.…”

Section: Related Workmentioning

confidence: 99%

Abstraction of man-made shapes

et al. 2009

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Figure 1: Given three-dimensional models of man-made objects, often containing multiple self-intersecting components, we extract characteristic curve networks along with auxiliary normal information providing a compact vector representation of the main model features and use those to generate 3D abstractions capturing the essence of the inputs. AbstractMan-made objects are ubiquitous in the real world and in virtual environments. While such objects can be very detailed, capturing every small feature, they are often identified and characterized by a small set of defining curves. Compact, abstracted shape descriptions based on such curves are often visually more appealing than the original models, which can appear to be visually cluttered. We introduce a novel algorithm for abstracting three-dimensional geometric models using characteristic curves or contours as building blocks for the abstraction. Our method robustly handles models with poor connectivity, including the extreme cases of polygon soups, common in models of man-made objects taken from online repositories. In our algorithm, we use a two-step procedure that first approximates the input model using a manifold, closed envelope surface and then extracts from it a hierarchical abstraction curve network along with suitable normal information. The constructed curve networks form a compact, yet powerful, representation for the input shapes, retaining their key shape characteristics while discarding minor details and irregularities.

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Section: Related Workmentioning

confidence: 99%

Abstraction of man-made shapes

et al. 2009

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“…Other avenues explored for abstraction include rule-based simplification [Brown et al 1993], user-guided parametric models [Falcid et al 1998], or topology-based inference [Biasotti et al 2000]. These approaches aim to abstract the content of 2D imagery using segmentation, clustering, and scale-space processing to find regions or lines that can be omitted from imagery.…”

Section: Non-photorealistic Renderingmentioning

confidence: 99%