Journal
ACM TRANSACTIONS ON GRAPHICS
Volume 32, Issue 6, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2508363.2508393
Keywords
Projective shape analysis; semantic segmentation and labeling; bilateral symmetric; Hausdorff distance; shape matching
Categories
Funding
- NSFC [61202222, 61232011, 61025012, 61202223]
- 863 Program [2013AA01A604]
- Guangdong Science and Technology Program [2011B050200007]
- Shenzhen Science and Innovation Program [CXB201104220029A, ZD201111080115A, KC2012JSJS0019A]
- Natural Science and Engineering Research Council of Canada [293127, 611370]
- Israel Science Foundation
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We introduce projective analysis 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 bi-class Hausdorff distance. The distance is topology-aware by accounting for internal holes in the 2D figures and it is applied to piecewise-linearly warped 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.
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