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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Depth Estimation by Convolutional Neural Networks
 Ivanecký, Ján ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
This thesis deals with depth estimation using convolutional neural networks. I propose a three-part model as a solution to this problem. The model contains a global context network which estimates coarse depth structure of the scene, a gradient network which estimates depth gradients and a refining network which utilizes the outputs of previous two networks to produce the final depth map. Additionally, I present a normalized loss function for training neural networks. Applying normalized loss function results in better estimates of the scene's relative depth structure, however it results in a loss of information about the absolute scale of the scene.
Detailed record
Depth Estimation by Convolutional Neural Networks
 Ivanecký, Ján ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
This thesis deals with depth estimation using convolutional neural networks. I propose a three-part model as a solution to this problem. The model contains a global context network which estimates coarse depth structure of the scene, a gradient network which estimates depth gradients and a refining network which utilizes the outputs of previous two networks to produce the final depth map. Additionally, I present a normalized loss function for training neural networks. Applying normalized loss function results in better estimates of the scene's relative depth structure, however it results in a loss of information about the absolute scale of the scene.
Detailed record

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