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R.: Holistic scene understanding for 3D object detection with rgbd cameras
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| Venue: | In: ICCV. (2013 |
| Citations: | 21 - 4 self |
BibTeX
@INPROCEEDINGS{Lin_r.:holistic,
author = {Dahua Lin and Sanja Fidler and Raquel Urtasun},
title = {R.: Holistic scene understanding for 3D object detection with rgbd cameras},
booktitle = {In: ICCV. (2013},
year = {}
}
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Abstract
In this paper, we tackle the problem of indoor scene un-derstanding using RGBD data. Towards this goal, we pro-pose a holistic approach that exploits 2D segmentation, 3D geometry, as well as contextual relations between scenes and objects. Specifically, we extend the CPMC [3] frame-work to 3D in order to generate candidate cuboids, and develop a conditional random field to integrate informa-tion from different sources to classify the cuboids. With this formulation, scene classification and 3D object recognition are coupled and can be jointly solved through probabilis-tic inference. We test the effectiveness of our approach on the challenging NYU v2 dataset. The experimental results demonstrate that through effective evidence integration and holistic reasoning, our approach achieves substantial im-provement over the state-of-the-art. 1.
Keyphrases
rgbd camera holistic scene object detection different source probabilis-tic inference holistic approach candidate cuboid object recognition indoor scene substantial im-provement contextual relation effective evidence integration holistic reasoning nyu v2 dataset scene classification experimental result rgbd data conditional random field
