loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Ahmed J. Afifi 1 ; Olaf Hellwich 1 and Toufique Ahmed Soomro 2

Affiliations: 1 Computer Vision and Remote Sensing, Technische Universität Berlin, Berlin, Germany ; 2 Electronic Engineering Department, Quaid-e-Awam University of Engineering and Technology, Larkana Campus, Pakistan

Keyword(s): Convolutional Neural Networks, CNN, Depth Estimation, Single View.

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 informat ion 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.19.31.73

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 205-214. DOI: 10.5220/0009356102050214

@conference{visapp20,
author={Ahmed J. Afifi. and Olaf Hellwich. and Toufique Ahmed 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 (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={205-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009356102050214},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Mini V-Net: Depth Estimation from Single Indoor-Outdoor Images using Strided-CNN
SN - 978-989-758-402-2
IS - 2184-4321
AU - Afifi, A.
AU - Hellwich, O.
AU - Soomro, T.
PY - 2020
SP - 205
EP - 214
DO - 10.5220/0009356102050214
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic