Please use this identifier to cite or link to this item:
https://doi.org/10.1109/CVPR.2013.8
DC Field | Value | |
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dc.title | 3D-based reasoning with blocks, support, and stability | |
dc.contributor.author | Jia Z. | |
dc.contributor.author | Gallagher A. | |
dc.contributor.author | Saxena A. | |
dc.contributor.author | Chen T. | |
dc.date.accessioned | 2018-08-21T04:55:42Z | |
dc.date.available | 2018-08-21T04:55:42Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | Jia Z., Gallagher A., Saxena A., Chen T. (2013). 3D-based reasoning with blocks, support, and stability. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 1-8. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2013.8 | |
dc.identifier.issn | 10636919 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/146097 | |
dc.description.abstract | 3D volumetric reasoning is important for truly understanding a scene. Humans are able to both segment each object in an image, and perceive a rich 3D interpretation of the scene, e.g., the space an object occupies, which objects support other objects, and which objects would, if moved, cause other objects to fall. We propose a new approach for parsing RGB-D images using 3D block units for volumetric reasoning. The algorithm fits image segments with 3D blocks, and iteratively evaluates the scene based on block interaction properties. We produce a 3D representation of the scene based on jointly optimizing over segmentations, block fitting, supporting relations, and object stability. Our algorithm incorporates the intuition that a good 3D representation of the scene is the one that fits the data well, and is a stable, self-supporting (i.e., one that does not topple) arrangement of objects. We experiment on several datasets including controlled and real indoor scenarios. Results show that our stability-reasoning framework improves RGB-D segmentation and scene volumetric representation. | |
dc.source | Scopus | |
dc.type | Conference Paper | |
dc.contributor.department | OFFICE OF THE PROVOST | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.1109/CVPR.2013.8 | |
dc.description.sourcetitle | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | |
dc.description.page | 1-8 | |
dc.description.coden | PIVRE | |
dc.published.state | published | |
Appears in Collections: | Staff Publications |
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