Abstract
Object detection and pose estimation still are very challenging tasks for robots. One common problem for many processing pipelines is the big amount of object data, e.g. often it is not known beforehand how many objects and which object classes can occur in the surrounding environment of a robot. Especially available model-based object detection pipelines often focus on a few different object classes. However, new deep learning algorithms have been developed in the last years. They are able to handle a big amount of data and can easily distinguish between different object classes. The drawback is the high amount of training data needed. In general, both approaches have different advantages and disadvantages. Thus, this paper presents a new way to combine model-based 6D pose estimation with deep learning to reduce time for training and to improve the 6D pose estimation pipeline.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Salti, S., Tombari, F., Di Stefano, L.: Shot: unique signatures of histograms for surface and texture description. Comput. Vis. Image Underst. 125, 251–264 (2014)
Hinterstoisser, S., Lepetit, V., Rajkumar, N., Konolige, K.: Going further with point pair features, CoRR (2017)
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: Unified, real-time object detection, CoRR (2015)
Kisner, H., Thomas, U.: Efficient object pose estimation in 3D point clouds using sparse Hash-Maps and Point-Pair features. In: 50th International Symposium on Robotics (ISR 2018), Munich, Germany, June 2018
Kisner, H., Thomas, U.: Segmentation of 3D point clouds using a new spectral clustering algorithm without a-priori knowledge. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2018)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)
Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111, 98–136 (2015)
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation, CoRR (2013)
Abdulla, W.: Mask R-CNN for object detection and instance segmentation on keras and tensorflow (2017). https://github.com/matterport/Mask_RCNN
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Computer Vision – ECCV 2014 (2014)
Hinterstoisser, S., Lepetit, V., Wohlhart, P., Konolige, K.: On pre-trained image features and synthetic images for deep learning, CoRR (2017)
Doumanoglou, A., Kouskouridas, R., Malassiotis, S., Kim, T.: 6D object detection and next-best-view prediction in the crowd, CoRR (2015)
Acknowledgements
This work is supported by the European Social Fund (ESF) and the Free State of Saxony.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kisner, H., Schreiter, T., Thomas, U. (2020). Learning to Predict 2D Object Instances by Applying Model-Based 6D Pose Estimation. In: Berns, K., Görges, D. (eds) Advances in Service and Industrial Robotics. RAAD 2019. Advances in Intelligent Systems and Computing, vol 980. Springer, Cham. https://doi.org/10.1007/978-3-030-19648-6_57
Download citation
DOI: https://doi.org/10.1007/978-3-030-19648-6_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19647-9
Online ISBN: 978-3-030-19648-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)