Abstract
Several approaches using unsupervised methods have been proposed recently to perform the task of depth prediction with higher accuracy. However, none of these approaches are flexible enough to be deployed in the real-time environment with limited computational capabilities. Inference latency is a major factor that limits the application of such methods to the real world scenarios where high end GPUs cannot be deployed. Six models based on three approaches are proposed in this work to reduce inference latency of depth prediction solutions without losing accuracy. The proposed solutions can be deployed in real-world applications with limited computational power and memory. The new models are also compared with the models recently proposed in literature to establish a state of the art depth prediction model that can be used in real-time.
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Siddiqui, M.A., Jain, A., Gour, N., Khanna, P. (2020). Unsupervised Single-View Depth Estimation for Real Time Inference. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_9
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