Holistic and local patch framework for 6D object pose estimation in RGB-D images-Reference-Cited by-同舟云学术

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Holistic and local patch framework for 6D object pose estimation in RGB-D images

Published:2019-03 Issue: Volume:180 Page:59-73
ISSN:1077-3142
Container-title:Computer Vision and Image Understanding
language:en
Short-container-title:Computer Vision and Image Understanding

Author:

Zhang Haoruo,Cao Qixin

Funder

National Natural Science Foundation of China

Shanghai Kangqiao Robotic Industry Development Joint Research Centre, China

Publisher

Elsevier BV

Subject

Computer Vision and Pattern Recognition,Signal Processing,Software

Reference52 articles.

1. Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C., 2014. Learning 6D object pose estimation using 3D object coordinates. In: European Conference on Computer Vision, ECCV, pp. 536–551.

2. Brachmann, E., Michel, F., Krull, A., Yang, M., Gumhold, S., Rother, C., 2016. Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image. In: IEEE Computer Vision and Pattern Recognition, CVPR, pp. 3364–3372.

3. Cai, H., Werner, T., Matas, J., 2013. Fast detection of multiple textureless 3-D objects. In: IEEE International Conference on Computer Vision, ICCV, pp. 103–112.

4. Choi, C., Christensen, H., 2012. 3D pose estimation of daily objects using an RGB-D camera. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 3342–3349.

5. Crivellaro, A., Rad, M., Verdie, Y., Yi, K., Fua, P., Lepetit, V., 2015. A novel representation of parts for accurate 3D object detection and tracking in monocular images. In: IEEE International Conference on Computer Vision, ICCV, pp. 4391–4399.

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