High-level Understanding Of 3D Models Based On Style Analysis

In order to solve the problem of translating the high-level semantic information of style into low-level objective descriptions,this paper presents a solution for co-locating style-defining elements over a set of 3D shapes.These elements are local regions and sub-structures of a shape which define its style.Our goal is to translate high-level style descriptions,such as ”Ming” or “European” for furniture models,into explicit and localized regions over the geometric models that characterize each style.For each style,the set of style-defining elements is defined as the union of all the elements that are able to discriminate the style.Another property of the style-defining elements is that they are frequently-occurring,reflecting shape characteristics that appear across multiple shapes of the same style.Given an input set of 3D shapes spanning multiple categories and styles,where the shapes are grouped according to their style labels,we perform a crosscategory co-analysis of the shape set to learn and spatially locate a set of defining elements for each style.This is accomplished by first sampling a large number of candidate geometric elements,and then iteratively applying feature selection to the candidates,to extract style-discriminating elements until no additional elements can be found.Thus,for each style label,we obtain sets of discriminative elements that together form the superset of defining elements for the style.We demonstrate that the co-location of style-defining elements al ows us to address tasks such as style classification,and enables a variety of applications such as style-revealing view selection,styleaware sampling,and style-driven modeling for 3D shapes.