SGM-MDE: Semi-global optimization for classification-based monocular depth estimation
In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020-10-24
Online
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Zugriff:
Depth estimation plays a crucial role in robotic applications that require environment perception. With the introduction of convolutional neural networks, monocular depth estimation (MDE) methods have become viable alternatives to LiDAR and stereo reconstruction-based solutions. Such methods require less equipment, fewer resources and do not need additional sensor alignment requirements. However, due to the ill-posed formulation of MDE, such algorithms can only rely on learning mechanisms, which makes them less reliable and less robust.In this work we propose a novel method to cope with the lack of geometric constraints inherent to monocular depth computation. Towards this goal, we initially mathematically transform the feature vectors from the last layer inside a MDE CNN such that a 3D stereo-like cost volume is generated. We then adapt the semi-global stereo optimization to the aforementioned volume, global consistency of the map being ensured. Furthermore, we enhance the results by adding a sub-pixel stereo post-processing be means of interpolation functions, a larger range of depth values being obtained. Our method can be applied to any classification-based MDE, experiments showing an increase in accuracy with an additional time cost of only 8 ms on a regular GPU, making the technique usable for real-time applications.
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SGM-MDE: Semi-global optimization for classification-based monocular depth estimation
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Autor/in / Beteiligte Person: | Nedevschi, Sergiu ; Miclea, Vlad-Cristian |
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Zeitschrift: | 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020-10-24 |
Veröffentlichung: | IEEE, 2020 |
Medientyp: | unknown |
DOI: | 10.1109/iros45743.2020.9340766 |
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