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Graph."],"published-print":{"date-parts":[[2013,11]]},"abstract":"<jats:p>We introduce<jats:italic>projective analysis<\/jats:italic>for semantic segmentation and labeling of 3D shapes. The analysis treats an input 3D shape as a collection of 2D projections, labels each projection by transferring knowledge from existing labeled images, and back-projects and fuses the labelings on the 3D shape. The image-space analysis involves matching projected binary images of 3D objects based on a novel<jats:italic>bi-class Hausdorff distance<\/jats:italic>. The distance is topology-aware by accounting for internal holes in the 2D figures and it is applied to<jats:italic>piecewise-linearly warped<\/jats:italic>object projections to compensate for part scaling and view discrepancies. Projective analysis simplifies the processing task by working in a lower-dimensional space, circumvents the requirement of having complete and well-modeled 3D shapes, and addresses the data challenge for 3D shape analysis by leveraging the massive available image data. A large and dense labeled set ensures that the labeling of a given projected image can be inferred from closely matched labeled images. We demonstrate semantic labeling of imperfect (e.g., incomplete or self-intersecting) 3D models which would be otherwise difficult to analyze without taking the projective analysis approach.<\/jats:p>","DOI":"10.1145\/2508363.2508393","type":"journal-article","created":{"date-parts":[[2013,11,6]],"date-time":"2013-11-06T14:09:19Z","timestamp":1383746959000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":51,"title":["Projective analysis for 3D shape segmentation"],"prefix":"10.1145","volume":"32","author":[{"given":"Yunhai","family":"Wang","sequence":"first","affiliation":[{"name":"Shenzhen VisuCA Key Lab\/SIAT"}]},{"given":"Minglun","family":"Gong","sequence":"additional","affiliation":[{"name":"Shenzhen VisuCA Key Lab\/SIAT and Memorial University of Newfoundland"}]},{"given":"Tianhua","family":"Wang","sequence":"additional","affiliation":[{"name":"Jilin University and Shenzhen VisuCA Key Lab\/SIAT"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Tel-Aviv University"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}]},{"given":"Baoquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Shenzhen VisuCA Key Lab\/SIAT and Shangdong University"}]}],"member":"320","published-online":{"date-parts":[[2013,11]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"IEEE Workshop on Robust Computer Vision, 59--78","author":"Baddeley A. 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