Parameterization of Ambiguity in Monocular Depth Prediction
(2021) 9th International Conference on 3D Vision, 3DV 2021 p.761-770- Abstract
Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these use recognition of high level image features to predict reasonably looking depth maps,the result often has poor metric accuracy. Moreover,the standard feed forward architecture does not allow modification of the prediction based on cues other than the image.In this paper we relax the monocular depth estimation task by proposing a network that allows us to complement image features with a set of auxiliary variables. These allow disambiguation when image features are not enough to accurately pinpoint the exact depth map and can be thought of as a low dimensional parameterization of the surfaces that are reasonable... (More)
Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these use recognition of high level image features to predict reasonably looking depth maps,the result often has poor metric accuracy. Moreover,the standard feed forward architecture does not allow modification of the prediction based on cues other than the image.In this paper we relax the monocular depth estimation task by proposing a network that allows us to complement image features with a set of auxiliary variables. These allow disambiguation when image features are not enough to accurately pinpoint the exact depth map and can be thought of as a low dimensional parameterization of the surfaces that are reasonable monocular predictions. By searching the parameterization we can combine monocular estimation with traditional photoconsistency or geometry based methods to achieve both visually appealing and metrically accurate surface estimations. Since we relax the problem we are able to work with smaller networks than current architectures. In addition we design a self-supervised training scheme,eliminating the need for ground truth image depth-map pairs. Our experimental evaluation shows that our method generates more accurate depth maps and generalizes better than competing state-of-the-art approaches.
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- author
- Persson, Patrik LU ; Ostrom, Linn LU ; Olsson, Carl LU and Astrom, Kalle LU
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- 3D Reconstruction, Machine Learning, Monocular Depth Parameterization
- host publication
- Proceedings - 2021 International Conference on 3D Vision, 3DV 2021
- pages
- 10 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 9th International Conference on 3D Vision, 3DV 2021
- conference location
- Virtual, Online, United Kingdom
- conference dates
- 2021-12-01 - 2021-12-03
- external identifiers
-
- scopus:85125009837
- ISBN
- 9781665426886
- DOI
- 10.1109/3DV53792.2021.00085
- language
- English
- LU publication?
- yes
- id
- 215fdfde-0a06-4dba-ad8e-421cde7baa5d
- date added to LUP
- 2022-04-13 17:06:02
- date last changed
- 2022-05-07 00:34:32
@inproceedings{215fdfde-0a06-4dba-ad8e-421cde7baa5d, abstract = {{<p>Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these use recognition of high level image features to predict reasonably looking depth maps,the result often has poor metric accuracy. Moreover,the standard feed forward architecture does not allow modification of the prediction based on cues other than the image.In this paper we relax the monocular depth estimation task by proposing a network that allows us to complement image features with a set of auxiliary variables. These allow disambiguation when image features are not enough to accurately pinpoint the exact depth map and can be thought of as a low dimensional parameterization of the surfaces that are reasonable monocular predictions. By searching the parameterization we can combine monocular estimation with traditional photoconsistency or geometry based methods to achieve both visually appealing and metrically accurate surface estimations. Since we relax the problem we are able to work with smaller networks than current architectures. In addition we design a self-supervised training scheme,eliminating the need for ground truth image depth-map pairs. Our experimental evaluation shows that our method generates more accurate depth maps and generalizes better than competing state-of-the-art approaches. </p>}}, author = {{Persson, Patrik and Ostrom, Linn and Olsson, Carl and Astrom, Kalle}}, booktitle = {{Proceedings - 2021 International Conference on 3D Vision, 3DV 2021}}, isbn = {{9781665426886}}, keywords = {{3D Reconstruction; Machine Learning; Monocular Depth Parameterization}}, language = {{eng}}, pages = {{761--770}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Parameterization of Ambiguity in Monocular Depth Prediction}}, url = {{http://dx.doi.org/10.1109/3DV53792.2021.00085}}, doi = {{10.1109/3DV53792.2021.00085}}, year = {{2021}}, }