Uncertainty-aware CNNs for depth completion: Uncertainty from beginning to end

Abdelrahman Eldesokey, Michael Felsberg, Karl Holmquist, Michael Persson

Research output: Contribution to conferencePaperpeer-review

89 Scopus citations

Abstract

The focus in deep learning research has been mostly to push the limits of prediction accuracy. However, this was often achieved at the cost of increased complexity, raising concerns about the interpretability and the reliability of deep networks. Recently, an increasing attention has been given to untangling the complexity of deep networks and quantifying their uncertainty for different computer vision tasks. Differently, the task of depth completion has not received enough attention despite the inherent noisy nature of depth sensors. In this work, we thus focus on modeling the uncertainty of depth data in depth completion starting from the sparse noisy input all the way to the final prediction. We propose a novel approach to identify disturbed measurements in the input by learning an input confidence estimator in a self-supervised manner based on the normalized convolutional neural networks (NCNNs). Further, we propose a probabilistic version of NCNNs that produces a statistically meaningful uncertainty measure for the final prediction. When we evaluate our approach on the KITTI dataset for depth completion, we outperform all the existing Bayesian Deep Learning approaches in terms of prediction accuracy, quality of the uncertainty measure, and the computational efficiency. Moreover, our small network with 670k parameters performs on-par with conventional approaches with millions of parameters. These results give strong evidence that separating the network into parallel uncertainty and prediction streams leads to state-of-the-art performance with accurate uncertainty estimates.

Original languageEnglish (US)
Pages12011-12020
Number of pages10
DOIs
StatePublished - 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: Jun 14 2020Jun 19 2020

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020
Country/TerritoryUnited States
CityVirtual, Online
Period06/14/2006/19/20

Bibliographical note

Funding Information:
We proposed a self-supervised approach for estimating the input confidence for sparse data based on the NCNNs. We also introduced a probabilistic version of NCNNs that enable the to output meaningful uncertainty measures. Experiments on the KITTI dataset for unguided depth completion showed that our small network with 670k parameters achieves state-of-the-art results in terms of prediction accuracy and it provides an accurate uncertainty measure. When compared against the existing probabilistic method for dense problems, our proposed approach outperforms all of them in terms of the prediction accuracy, the quality of the uncertainty measure, and the computational efficiency. Moreover, we showed that our approach can be applied to other sparse problems as well. These results demonstrate the gains from adhering to the signal/uncertainty philosophy compared to conventional black-box models. Acknowledgments: This work was supported by the Wal-lenberg AI, Autonomous Systems and Software Program (WASP) and Swedish Research Council grant 2018-04673.

Publisher Copyright:
© 2020 IEEE.

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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