Abstract
Recently convolutional neural network has achieved great success in the field of object recognition, but it is a hard work to get enough labeled data for training a neural network, especially for novel object instances. In this paper, we address this problem by generating synthetic images in simulation environment. We propose a method that generates a large amount multi-view synthetic images automatically to avoid manual collection and annotation. When applying our method to object recognition in real scenarios, the robot picks up the object first, then gets the object images using the same method of getting training images, which reduces the domain gap between real images and synthetic images. Experiments show that our method can recognize various objects with different poses efficiently.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, T.W., Lin, W.C.: A neural network approach to CSG-based 3-D object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 16(7), 719–726 (1991)
Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594–611 (2006)
Fei-Fei, L.: Knowledge transfer in learning to recognize visual objects classes. In: Proceedings of the International Conference on Development and Learning (ICDL), p. 11 (2006)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Liebelt, J., Schmid, C.: Multi-view object class detection with a 3D geometric model. In: Computer Vision and Pattern Recognition, pp. 1688–1695 (2010)
Liebelt, J., Schmid, C., Schertler, K.: Viewpoint-independent object class detection using 3D feature maps. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2010)
Peng, X., Sun, B., Ali, K., Saenko, K.: Exploring invariances in deep convolutional neural networks using synthetic images. Eprint Arxiv, pp. 1278–1286 (2014)
Peng, X., Sun, B., Ali, K., Saenko, K.: Learning deep object detectors from 3D models, pp. 1278–1286 (2014)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Sarkar, K., Pagani, A., Stricker, D.: Feature-augmented trained models for 6DOF object recognition and camera calibration. In: International Conference on Computer Vision Theory and Applications, pp. 632–640 (2016)
Sarkar, K., Varanasi, K., Stricker, D., Sarkar, K., Varanasi, K., Stricker, D.: Trained 3D models for CNN based object recognition. In: International Conference on Computer Vision Theory and Applications, pp. 130–137 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
Smith, L.N.: Cyclical learning rates for training neural networks. Computer Science pp. 464–472 (2015)
Stark, M., Goesele, M., Schiele, B.: Back to the future: Learning shape models from 3d cad data. In: Proceedings of the British Machine Vision Conference, BMVC 2010, Aberystwyth, UK, 31 August–3 September 2010, pp. 1–11 (2010)
Su, H., Maji, S., Kalogerakis, E., Learnedmiller, E.: Multi-view convolutional neural networks for 3d shape recognition, pp. 945–953 (2015)
Sun, M., Su, H., Savarese, S., Li, F.F.: A multi-view probabilistic model for 3D object classes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1247–1254 (2009)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Zha, H., Nanamegi, H., Nagata, T.: 3-D object recognition from range images by using a model-based hopfield-style matching algorithm. In: International Conference on Pattern Recognition, vol. 4, pp. 111–116 (1996)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 61573333, U1613216) and the Joint Funds of the National Natural Science Foundation of China (Grand No. U1613216).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Jin, Z., Cui, G., Chen, G., Chen, X. (2018). A Multi-view Images Generation Method for Object Recognition. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10985. Springer, Cham. https://doi.org/10.1007/978-3-319-97589-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-97589-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97588-7
Online ISBN: 978-3-319-97589-4
eBook Packages: Computer ScienceComputer Science (R0)