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Cited by 60Advanced Vision and ImagingRobot Manipulation and LearningRobotics and Sensor-Based Localization

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Multi-view 6D Object Pose Estimation and Camera Motion Planning Using RGBD Images

Sock, Juil, Kasaei, S. Hamidreza, Lopes, Luis Seabra, Kim, Tae-Kyun·2017·2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
DOI 10.1109/iccvw.2017.260
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Abstract

Recovering object pose in a crowd is a challenging task due to severe occlusions and clutters. In active scenario, whenever an observer fails to recover the poses of objects from the current view point, the observer is able to determine the next view position and captures a new scene from another view point to improve the knowledge of the environment, which may reduce the 6D pose estimation uncertainty. We propose a complete active multi-view framework to recognize 6DOF pose of multiple object instances in a crowded scene. We include several components in active vision setting to increase the accuracy: Hypothesis accumulation and verification combines single-shot based hypotheses estimatedfrom previous views and extract the most likely set of hypotheses; an entropy-based Next-Best-View prediction generates next camera position to capture new data to increase the performance; camera motion planning plans the trajectory of the camera based on the view entropy and the cost of movement. Different approaches for each component are implemented and evaluated to show the increase in performance.

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1 of 1 references verified0 citation errors · 0 not checkable

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  • Works considering deformable objects and a moving camera commonly reside in active vision and do not address the interaction aspect.

    “Works considering deformable objects and a moving camera reside commonly to active vision and do not address the interaction aspect [19], [20], [21], [22].”

    From Interactive Perception for Deformable Object Manipulation (2024) · cites this paper as [19]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 23

  1. leibe, 2016, Integration of Probabilistic Pose Estimates from Multiple Views, European Conference on Computer Vision ECCV 2016, 154
  2. 10.1007/s11263-009-0275-4 · DOI · Paper page
  3. 10.1145/358669.358692 · DOI · Paper page
  4. hinterstoisser, 2011, Multimodal templates for real-time detection of texture-less objects in heavily clutteredscenes, Proceedings of the IEEE International Conference on Computer Vision, 858
  5. 10.1109/CVPR.2010.5539908 · DOI
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  11. chiu, 1994, Fuzzy model identification based on cluster estimation, Journal of Intelligent and Fuzzy Systems, 2, 267, 10.3233/IFS-1994-2306 · DOI
  12. brachmann, 2014, Learning 6D object pose estimation using 3D object coordinates, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8690, 536
  13. 10.1109/ROBOT.2010.5509615 · DOI
  14. 10.1109/ROBOT.2010.5509644 · DOI
  15. doumanoglou, 2015, 6D Object Detection and Next-Best-View Prediction in the Crowd, ArXiv, 6, 3583
  16. biasotti, 2013, Sketch-based 3d model retrieval by viewpoint entropy-based adaptive view clustering
  17. 10.1109/CVPR.2016.390 · DOI
  18. 10.1007/978-3-642-33712-3_37 · DOI
  19. 10.1007/978-3-642-33868-7_10 · DOI
  20. 10.1109/IROS.2012.6386109 · DOI · Paper page
  21. tejani, 2014, Latent-class Hough forests for 3D object detection and pose estimation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8694, 462
  22. wu, 2015, 3d shapenets: A deep representation for volumetric shapes, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1912
  23. viksten, 2006, Increasing pose estimation performance using multicue integration, Proceedings - IEEE International Conference on Robotics and Automation, 2006, 3760

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Metadata

Authors
Sock, J., Kasaei, S. H., Lopes, L. S., & Kim, T.-K.
Affiliations
Imperial College London (GB)
ORCID
Venue
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Publisher
IEEE
Published
2017-10
Pages
2228-2235
ISSN
2473-9936 (ISSN-L)
DOI
10.1109/iccvw.2017.260
OpenAlex
W2768890541
Cited by
60
References
1
Citations by year
2026: 2 · 2025: 5 · 2024: 5 · 2023: 3 · 2022: 8 · 2021: 15 · 2020: 8 · 2019: 11 · 2018: 2 · 2017: 1
FWCI
1.75
Type
conference paper
Language
en
Open access
closed
Locations
IEEE International Conference on Computer Vision Workshops (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
Keywords
6D pose estimation, multi-view pose estimation, active vision, RGBD images, crowded scenes, occlusion, next-best-view selection

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