Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107977
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Type: | Conference paper |
Title: | Hierarchical higher-order regression forest fields: an application to 3D indoor scene labelling |
Author: | Pham, T. Reid, I. Latif, Y. Gould, S. |
Citation: | Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, 2015, vol.2015 International Conference on Computer Vision, ICCV 2015, pp.2246-2254 |
Publisher: | IEEE |
Issue Date: | 2015 |
Series/Report no.: | IEEE International Conference on Computer Vision |
ISBN: | 9781467383912 |
ISSN: | 1550-5499 |
Conference Name: | 2015 IEEE International Conference on Computer Vision (ICCV 2015) (7 Dec 2015 - 13 Dec 2015 : Santiago, CHILE) |
Statement of Responsibility: | Trung T. Pham, Ian Reid, Yasir Latif, Stephen Gould |
Abstract: | This paper addresses the problem of semantic segmentation of 3D indoor scenes reconstructed from RGB-D images. Traditionally label prediction for 3D points is tackled by employing graphical models that capture scene features and complex relations between different class labels. However, the existing work is restricted to pairwise conditional random fields, which are insufficient when encoding rich scene context. In this work we propose models with higher-order potentials to describe complex relational information from the 3D scenes. Specifically, we relax the labelling problem to a regression, and generalize the higher-order associative Pn Potts model to a new family of arbitrary higherorder models based on regression forests. We show that these models, like the robust Pn models, can still be decomposed into the sum of pairwise terms by introducing auxiliary variables. Moreover, our proposed higher-order models also permit extension to hierarchical random fields, which allows for the integration of scene context and features computed at different scales. Our potential functions are constructed based on regression forests encoding Gaussian densities that admit efficient inference. The parameters of our model are learned from training data using a structured learning approach. Results on two datasets show clear improvements over current state-of-the-art methods. |
Keywords: | Three-dimensional displays, labeling, semantics, solid modeling, computational modeling, robustness, context modeling |
Rights: | © 2015 IEEE |
DOI: | 10.1109/ICCV.2015.259 |
Grant ID: | http://purl.org/au-research/grants/arc/CE140100016 http://purl.org/au-research/grants/arc/FL130100102 |
Published version: | http://dx.doi.org/10.1109/iccv.2015.259 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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RA_hdl_107977.pdf Restricted Access | Restricted Access | 1.22 MB | Adobe PDF | View/Open |
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