logo

SCIENTIA SINICA Informationis, Volume 51 , Issue 5 : 795(2021) https://doi.org/10.1360/SSI-2020-0323

Efficient accelerator architecture for optical flow estimation

More info
  • ReceivedMay 12, 2020
  • AcceptedOct 15, 2020
  • PublishedApr 8, 2021

Abstract


Funded by

国家自然科学基金(61804155,61834006,U1811264,61966009,U1711263)

广西可信软件重点实验室(kx202025)


References

[1] Menze M, Geiger A. Object scene flow for autonomous vehicles. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 3061--3070. Google Scholar

[2] Dosovitskiy A, Fischer P, Ilg E, et al. Flownet: learning optical flow with convolutional networks. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2758--2766. Google Scholar

[3] Ilg E, Mayer N, Saikia T, et al. Flownet 2.0: evolution of optical flow estimation with deep networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2462--2470. Google Scholar

[4] Ranjan A, Black M J. Optical flow estimation using a spatial pyramid network. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 4161--4170. Google Scholar

[5] Sun D, Yang X, Liu M Y, et al. Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 8934--8943. Google Scholar

[6] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 1--9. Google Scholar

[7] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 1520--1528. Google Scholar

[8] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012. 1097--1105. Google Scholar

[9] Chen T, Du Z, Sun N. DianNao. SIGARCH Comput Archit News, 2014, 42: 269-284 CrossRef Google Scholar

[10] Chen Y H, Krishna T, Emer J S. Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks. IEEE J Solid-State Circuits, 2017, 52: 127-138 CrossRef ADS Google Scholar

[11] Feng Y, Whatmough P, Zhu Y. ASV: accelerated stereo vision system. In: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019. 643--656. Google Scholar

[12] Liu B, Chen X, Han Y. Accelerating DNN-based 3D point cloud processing for mobile computing. Sci China Inf Sci, 2019, 62: 212206 CrossRef Google Scholar

[13] Banz C, Hesselbarth S, Flatt H, et al. Real-time stereo vision system using semi-global matching disparity estimation: architecture and FPGA-implementation. In: Proceedings of International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, 2010. 93--101. Google Scholar

[14] Yongbing Zhang , Debin Zhao , Jian Zhang . Interpolation-Dependent Image Downsampling. IEEE Trans Image Process, 2011, 20: 3291-3296 CrossRef ADS Google Scholar

[15] Muralimanohar N, Balasubramonian R, Jouppi N. Optimizing NUCA organizations and wiring alternatives for large caches with CACTI 6.0. In: Proceedings of IEEE/ACM International Symposium on Microarchitecture, 2007. 3--14. Google Scholar

[16] Geiger A, Lenz P, Stiller C. Vision meets robotics: The KITTI dataset. Int J Robotics Res, 2013, 32: 1231-1237 CrossRef Google Scholar

[17] Samajdar A, Zhu Y, Whatmough P, et al. SCALE-sim: systolic CNN accelerator simulator. 2018,. arXiv Google Scholar

qqqq

Contact and support