SBIN: A stereo disparity estimation network using binary convolutions



stereo vision, Computer vision, Embedded devices, Binary networks


Although the current advances on convolutional networks are outstanding, they mainly depend on extensive computational power, limiting the areas of applications. The latter applies for stereo disparity estimation, where current solutions can barely run on embedded devices. This work shows that it is possible to binarize an end-to-end stereo disparity network, which can be considered a step towards lightweight and potentially faster disparity estimation networks. This work shows the validity of the proposed approach through experimentation in two well-known datasets, sceneflow and kitti2012. The results show that a binary disparity model is possible but at the cost of performance. An EPE of 5.14 and 2.09 is achieved in sceneflow and kitti2012 accordingly.


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Author Biography

Cristhian Alejandro Aguilera, Universidad de los Lagos

Cristhian Aguilera received the B.S. degree in automation engineer from the Universidad del Bío-Bío, Concepción, Chile, in 2008, the MSc degree in computer vision from the Universitat Autónoma de Barcelona (UAB), Barcelona, Spain, 2014, and the PhD degree in Computer Science in 2017 from the same university. His current research focuses on the industrial application of machine learning, using images from one or multiples spectra.


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How to Cite

Aguilera, C. A. (2022). SBIN: A stereo disparity estimation network using binary convolutions. IEEE Latin America Transactions, 20(4), 693–699. Retrieved from