Please use this identifier to cite or link to this item: http://ri-ng.uaq.mx/handle/123456789/3397
Title: Monocular Depth Estimation with Convolutional Neural Networks on Embedded Systems
metadata.dc.creator: EDGAR RODRIGO LOPEZ SILVA
Keywords: OTRAS
metadata.dc.date: 7-Sep-2021
Description: Monocular depth estimation is becoming a very interesting problem in computer vision to solve due to the several tasks that require as an input the spatial structure of a scene, such as 3D reconstruction, 3D object detection, localization and mapping. The most effective techniques for monocular depth estimation are based on large deep learning-based architectures that cannot be deployed on systems with limited computational resources and therefore preventing its use in application fields where the advantages of monocular cameras (i.e., low cost, small size, low weight and low-energy consumption) could also be exploited. Under this context, the research of low-latency deep learning architectures for monocular depth estimation is a very promising topic for which just a few methods have been proposed until now. In this master thesis, a very low-latency fully convolutional network is proposed. The quantitative results on the NYU-Depth V2 dataset show that the proposed method is 1.6x faster than the state-of-the art related method while also reducing the RMSE metric by 1.16%.
URI: http://ri-ng.uaq.mx/handle/123456789/3397
Other Identifiers: monocular
depth
low-latency
convolutional
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