Shao, T, Monszpart, A, Zheng, Y et al. (4 more authors) (2014) Imagining the unseen: stability-based cuboid arrangements for scene understanding. ACM Transactions on Graphics, 33 (6). 209. ISSN 0730-0301
Abstract
Missing data due to occlusion is a key challenge in 3D acquisition, particularly in cluttered man-made scenes. Such partial information about the scenes limits our ability to analyze and understand them. In this work we abstract such environments as collections of cuboids and hallucinate geometry in the occluded regions by globally analyzing the physical stability of the resultant arrangements of the cuboids. Our algorithm extrapolates the cuboids into the un-seen regions to infer both their corresponding geometric attributes (e.g., size, orientation) and how the cuboids topologically interact with each other (e.g., touch or fixed). The resultant arrangement provides an abstraction for the underlying structure of the scene that can then be used for a range of common geometry processing tasks. We evaluate our algorithm on a large number of test scenes with varying complexity, validate the results on existing benchmark datasets, and demonstrate the use of the recovered cuboid-based structures towards object retrieval, scene completion, etc.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014, Association for Computing Machinery, Inc. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Graphics, https://doi.org/10.1145/10.1145/2661229.2661288. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | box world; proxy arrangements; physical stability; shape analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Aug 2018 08:29 |
Last Modified: | 17 Aug 2018 00:51 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Identification Number: | 10.1145/2661229.2661288 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134270 |