Ergebnisse für: Monocular Depth
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This work provides a method of producing dense depth estimations of a cystoscopic image through the embedding of a synthetic environment and using adversarial neural network training....
Depth sensing is pivotal in robotics; however, monocular depth estimation encounters significant challenges. Existing algorithms relying on large-scale labeled data and large Deep Convolutional Neural Networks (DCNNs) hinder real-world applications. We propose two lightweight architectures that achi...
This book explores a central question in the study of depth perception - 'does the visual system rely upon objective knowledge and subjective meaning to specify visual depth?' Linton advances an alternative interpretation to the generally accepted affirmative answer, according to which many of the a...
Vladislav Golyanik proposes several new methods for dense non-rigid structure from motion (NRSfM) as well as alignment of point clouds. The introduced methods improve the state of the art in various aspects, i.e. in the ability to handle inaccurate point tracks and 3D data with contaminations. NRSfM...
Vladislav Golyanik proposes several new methods for dense non-rigid structure from motion (NRSfM) as well as alignment of point clouds. The introduced methods improve the state of the art in various aspects, i.e. in the ability to handle inaccurate point tracks and 3D data with contaminations. NRSfM...
This book explores a central question in the study of depth perception - 'does the visual system rely upon objective knowledge and subjective meaning to specify visual depth?' Linton advances an alternative interpretation to the generally accepted affirmative answer, according to which many of the a...
This book explores a central question in the study of depth perception - 'does the visual system rely upon objective knowledge and subjective meaning to specify visual depth?' Linton advances an alternative interpretation to the generally accepted affirmative answer, according to which many of the a...