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|a 10.1109/TPAMI.2021.3124079
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|a eng
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| 100 |
1 |
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|a Bello, Juan Luis Gonzalez
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Self-Supervised Deep Monocular Depth Estimation With Ambiguity Boosting
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|c 2022
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|a Text
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 08.11.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We propose a novel two-stage training strategy with ambiguity boosting for the self-supervised learning of single view depths from stereo images. Our proposed two-stage learning strategy first aims to obtain a coarse depth prior by training an auto-encoder network for a stereoscopic view synthesis task. This prior knowledge is then boosted and used to self-supervise the model in the second stage of training in our novel ambiguity boosting loss. Our ambiguity boosting loss is a confidence-guided type of data augmentation loss that improves the accuracy and consistency of generated depth maps under several transformations of the single-image input. To show the benefits of the proposed two-stage training strategy with boosting, our two previous depth estimation (DE) networks, one with t-shaped adaptive kernels and the other with exponential disparity volumes, are extended with our new learning strategy, referred to as DBoosterNet-t and DBoosterNet-e, respectively. Our self-supervised DBoosterNets are competitive, and in some cases even better, compared to the most recent supervised SOTA methods, and are remarkably superior to the previous self-supervised methods for monocular DE on the challenging KITTI dataset. We present intensive experimental results, showing the efficacy of our method for the self-supervised monocular DE task
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|a Journal Article
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1 |
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|a Kim, Munchurl
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 12 vom: 02. Dez., Seite 9131-9149
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:44
|g year:2022
|g number:12
|g day:02
|g month:12
|g pages:9131-9149
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|u http://dx.doi.org/10.1109/TPAMI.2021.3124079
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