Simultaneous dense scene reconstruction and Object Labeling

Umar Asif; Mohammed Bennamoun; Ferdous Sohel

doi:10.1109/ICRA.2016.7487374

Simultaneous dense scene reconstruction and Object Labeling

Umar Asif, Mohammed Bennamoun, Ferdous Sohel

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

8 Citations (Scopus)

Abstract

This paper presents an efcient system for simultaneous dense scene reconstruction and object labeling in real-world environments (captured with an RGB-D sensor). The proposed system starts with the generation of object proposals in the scene. It then tracks spatio-temporally consistent object proposals across multiple frames and produces a dense reconstruction of the scene. In parallel, the proposed system uses an efcient inference algorithm, where object class probabilities are computed at an object-level and fused into a voxel-based prediction hypothesis modeled on the voxels of the reconstructed scene. Our extensive experiments using challenging RGB-D object and scene datasets, and live video streams from Microsoft Kinect show that the proposed system achieved competitive 3D scene reconstruction and object labeling results compared to the state-of-the-art methods.

Original language	English
Title of host publication	2016 IEEE International Conference on Robotics and Automation (ICRA)
Editors	Danica Kragic, Allison Okamura
Place of Publication	USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	2255-2262
Number of pages	8
ISBN (Electronic)	9781467380263
ISBN (Print)	9781467380256
DOIs	https://doi.org/10.1109/ICRA.2016.7487374
Publication status	Published - 8 Jun 2016
Event	2016 IEEE International Conference on Robotics and Automation: ICRA 2016 - Stockholm, Sweden Duration: 16 May 2016 → 21 May 2016

Publication series

Name	Proceedings - IEEE International Conference on Robotics and Automation
Volume	2016-June
ISSN (Print)	1050-4729

Conference

Conference	2016 IEEE International Conference on Robotics and Automation
Country/Territory	Sweden
City	Stockholm
Period	16/05/16 → 21/05/16

Access to Document

10.1109/ICRA.2016.7487374

1 Curtailed
2 Finished

Advanced 3D Computer Vision Algorithms for 'Find and Grasp' Future Robots
Bennamoun, M. (Investigator 01)
ARC Australian Research Council
1/01/15 → 31/12/20
Project: Research
A 3D Video-based Vision system for Future Robots
Bennamoun, M. (Investigator 01)
ARC Australian Research Council
1/01/11 → 31/12/15
Project: Research
Revocable 2D/3D Shape Based Multimodal Hand Biometrics for Personal Identification & Verification
Sohel, F. (Investigator 01)
ARC Australian Research Council
1/01/12 → 29/06/17
Project: Research

Cite this

Asif, U., Bennamoun, M., & Sohel, F. (2016). Simultaneous dense scene reconstruction and Object Labeling. In D. Kragic, & A. Okamura (Eds.), 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2255-2262). Article 7487374 (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2016-June). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICRA.2016.7487374

Asif, Umar ; Bennamoun, Mohammed ; Sohel, Ferdous. / Simultaneous dense scene reconstruction and Object Labeling. 2016 IEEE International Conference on Robotics and Automation (ICRA). editor / Danica Kragic ; Allison Okamura. USA : IEEE, Institute of Electrical and Electronics Engineers, 2016. pp. 2255-2262 (Proceedings - IEEE International Conference on Robotics and Automation).

@inproceedings{4e04002771914b9db05a47cc5259620b,

title = "Simultaneous dense scene reconstruction and Object Labeling",

abstract = "This paper presents an efcient system for simultaneous dense scene reconstruction and object labeling in real-world environments (captured with an RGB-D sensor). The proposed system starts with the generation of object proposals in the scene. It then tracks spatio-temporally consistent object proposals across multiple frames and produces a dense reconstruction of the scene. In parallel, the proposed system uses an efcient inference algorithm, where object class probabilities are computed at an object-level and fused into a voxel-based prediction hypothesis modeled on the voxels of the reconstructed scene. Our extensive experiments using challenging RGB-D object and scene datasets, and live video streams from Microsoft Kinect show that the proposed system achieved competitive 3D scene reconstruction and object labeling results compared to the state-of-the-art methods.",

author = "Umar Asif and Mohammed Bennamoun and Ferdous Sohel",

year = "2016",

month = jun,

day = "8",

doi = "10.1109/ICRA.2016.7487374",

language = "English",

isbn = "9781467380256",

series = "Proceedings - IEEE International Conference on Robotics and Automation",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "2255--2262",

editor = "Danica Kragic and Allison Okamura",

booktitle = "2016 IEEE International Conference on Robotics and Automation (ICRA)",

address = "United States",

note = "2016 IEEE International Conference on Robotics and Automation : ICRA 2016 ; Conference date: 16-05-2016 Through 21-05-2016",

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Asif, U, Bennamoun, M & Sohel, F 2016, Simultaneous dense scene reconstruction and Object Labeling. in D Kragic & A Okamura (eds), 2016 IEEE International Conference on Robotics and Automation (ICRA)., 7487374, Proceedings - IEEE International Conference on Robotics and Automation, vol. 2016-June, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 2255-2262, 2016 IEEE International Conference on Robotics and Automation, Stockholm, Sweden, 16/05/16. https://doi.org/10.1109/ICRA.2016.7487374

Simultaneous dense scene reconstruction and Object Labeling. / Asif, Umar; Bennamoun, Mohammed; Sohel, Ferdous.
2016 IEEE International Conference on Robotics and Automation (ICRA). ed. / Danica Kragic; Allison Okamura. USA: IEEE, Institute of Electrical and Electronics Engineers, 2016. p. 2255-2262 7487374 (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2016-June).

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

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T1 - Simultaneous dense scene reconstruction and Object Labeling

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AU - Bennamoun, Mohammed

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PY - 2016/6/8

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N2 - This paper presents an efcient system for simultaneous dense scene reconstruction and object labeling in real-world environments (captured with an RGB-D sensor). The proposed system starts with the generation of object proposals in the scene. It then tracks spatio-temporally consistent object proposals across multiple frames and produces a dense reconstruction of the scene. In parallel, the proposed system uses an efcient inference algorithm, where object class probabilities are computed at an object-level and fused into a voxel-based prediction hypothesis modeled on the voxels of the reconstructed scene. Our extensive experiments using challenging RGB-D object and scene datasets, and live video streams from Microsoft Kinect show that the proposed system achieved competitive 3D scene reconstruction and object labeling results compared to the state-of-the-art methods.

AB - This paper presents an efcient system for simultaneous dense scene reconstruction and object labeling in real-world environments (captured with an RGB-D sensor). The proposed system starts with the generation of object proposals in the scene. It then tracks spatio-temporally consistent object proposals across multiple frames and produces a dense reconstruction of the scene. In parallel, the proposed system uses an efcient inference algorithm, where object class probabilities are computed at an object-level and fused into a voxel-based prediction hypothesis modeled on the voxels of the reconstructed scene. Our extensive experiments using challenging RGB-D object and scene datasets, and live video streams from Microsoft Kinect show that the proposed system achieved competitive 3D scene reconstruction and object labeling results compared to the state-of-the-art methods.

U2 - 10.1109/ICRA.2016.7487374

DO - 10.1109/ICRA.2016.7487374

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BT - 2016 IEEE International Conference on Robotics and Automation (ICRA)

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T2 - 2016 IEEE International Conference on Robotics and Automation

Y2 - 16 May 2016 through 21 May 2016

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Asif U, Bennamoun M, Sohel F. Simultaneous dense scene reconstruction and Object Labeling. In Kragic D, Okamura A, editors, 2016 IEEE International Conference on Robotics and Automation (ICRA). USA: IEEE, Institute of Electrical and Electronics Engineers. 2016. p. 2255-2262. 7487374. (Proceedings - IEEE International Conference on Robotics and Automation). doi: 10.1109/ICRA.2016.7487374

Simultaneous dense scene reconstruction and Object Labeling

Abstract

Publication series

Conference

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Projects

Advanced 3D Computer Vision Algorithms for 'Find and Grasp' Future Robots

A 3D Video-based Vision system for Future Robots

Revocable 2D/3D Shape Based Multimodal Hand Biometrics for Personal Identification & Verification

Cite this