Room layout estimation with object and material attributes information using a spherical camera
Room layout estimation with object and material attributes information using a spherical camera
In this paper we propose a pipeline for estimating 3D room layout with object and material attribute prediction using a spherical stereo image pair. We assume that the room and objects can be represented as cuboids aligned to the main axes of the room coordinate (Manhattan world). A spherical stereo alignment algorithm is proposed to align two spherical images to the global world coordinate system. Depth information of the scene is estimated by stereo matching between images. Cubic projection images of the spherical RGB and estimated depth are used for object and material attribute detection. A single Convolutional Neural Network is designed to assign object and attribute labels to geometrical elements built from the spherical image. Finally simplified room layout is reconstructed by cuboid fitting. The reconstructed cuboid-based model shows the structure of the scene with object information and material attributes. © 2016 IEEE.
Neural networks, Spheres, Alignment algorithms, Attribute detections, Convolutional neural network, Depth information, Object information, Projection image, Spherical stereo, World coordinate systems, Stereo image processing
519-527
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
de Campos, Teofilo
6b6b4054-afdf-484d-b3dd-5f6ccbd082bd
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db
2016
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
de Campos, Teofilo
6b6b4054-afdf-484d-b3dd-5f6ccbd082bd
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db
Kim, H., de Campos, Teofilo and Hilton, Adrian
(2016)
Room layout estimation with object and material attributes information using a spherical camera.
In 2016 Fourth International Conference on 3D Vision (3DV).
IEEE.
.
(doi:10.1109/3DV.2016.83).
Record type:
Conference or Workshop Item
(Paper)
Abstract
In this paper we propose a pipeline for estimating 3D room layout with object and material attribute prediction using a spherical stereo image pair. We assume that the room and objects can be represented as cuboids aligned to the main axes of the room coordinate (Manhattan world). A spherical stereo alignment algorithm is proposed to align two spherical images to the global world coordinate system. Depth information of the scene is estimated by stereo matching between images. Cubic projection images of the spherical RGB and estimated depth are used for object and material attribute detection. A single Convolutional Neural Network is designed to assign object and attribute labels to geometrical elements built from the spherical image. Finally simplified room layout is reconstructed by cuboid fitting. The reconstructed cuboid-based model shows the structure of the scene with object information and material attributes. © 2016 IEEE.
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Published date: 2016
Additional Information:
cited By 10
Venue - Dates:
International Conference on 3D Vision, 2016-09-22
Keywords:
Neural networks, Spheres, Alignment algorithms, Attribute detections, Convolutional neural network, Depth information, Object information, Projection image, Spherical stereo, World coordinate systems, Stereo image processing
Identifiers
Local EPrints ID: 440597
URI: http://eprints.soton.ac.uk/id/eprint/440597
PURE UUID: 4eae1bd5-971a-4779-85ac-af462ccc509a
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Date deposited: 12 May 2020 16:30
Last modified: 17 Mar 2024 04:01
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Contributors
Author:
H. Kim
Author:
Teofilo de Campos
Author:
Adrian Hilton
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