Mini V-Net: Depth Estimation from Single Indoor-Outdoor Images using Strided-CNN

Ahmed Afifi, Olaf Hellwich, Toufique Soomro

Abstract

Depth estimation plays a vital role in many computer vision tasks including scene understanding and reconstruction. However, it is an ill-posed problem when it comes to estimating the depth from a single view due to the ambiguity and the lack of cues and prior knowledge. Proposed solutions so far estimate blurry depth images with low resolutions. Recently, Convolutional Neural Network (CNN) has been applied successfully to solve different computer vision tasks such as classification, detection, and segmentation. In this paper, we present a simple fully-convolutional encoder-decoder CNN for estimating depth images from a single RGB image with the same image resolution. For robustness, we leverage a non-convex loss function which is robust to the outliers to optimize the network. Our results show that a light simple model trained using a robust loss function outperforms or achieves comparable results with other methods quantitatively and qualitatively and produces better depth information of the scenes with sharper objects’ boundaries. Our model predicts the depth information in one shot with the same input resolution and without any further post-processing steps.

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Paper Citation


in Harvard Style

Afifi A., Hellwich O. and Soomro T. (2020). Mini V-Net: Depth Estimation from Single Indoor-Outdoor Images using Strided-CNN.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2, pages 205-214. DOI: 10.5220/0009356102050214


in Bibtex Style

@conference{visapp20,
author={Ahmed Afifi and Olaf Hellwich and Toufique Soomro},
title={Mini V-Net: Depth Estimation from Single Indoor-Outdoor Images using Strided-CNN},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={205-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009356102050214},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Mini V-Net: Depth Estimation from Single Indoor-Outdoor Images using Strided-CNN
SN - 978-989-758-402-2
AU - Afifi A.
AU - Hellwich O.
AU - Soomro T.
PY - 2020
SP - 205
EP - 214
DO - 10.5220/0009356102050214