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Bi-Projection Based Foreground-Aware Omnidirectional Depth Prediction

Qi Feng, Hubert P. H. Shum and Shigeo Morishima
Proceedings of the 2021 Visual Computing (VC), 2021

Bi-Projection Based Foreground-Aware Omnidirectional Depth Prediction

Abstract

Due to the increasing availability of commercial 360-degree cameras, accurate depth prediction for omnidirectional images can be beneficial to a wide range of applications including video editing and augmented reality. Regarding existing methods, some focus on learning high-quality global prediction while fail to capture detailed local features. Others suggest integrating local context into the learning procedure, they yet propose to train on non-foreground-aware databases. In this paper, we explore to simultaneously use equirectangular and cubemap projection to learn omnidirectional depth prediction from foreground-aware databases in a multi-task manner. Experimental results demonstrate improved performance when compared to the state-of-the-art.

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Citations

BibTeX

@inproceedings{feng21biprojection,
 author={Feng, Qi and Shum, Hubert P. H. and Morishima, Shigeo},
 booktitle={Proceedings of the 2021 Visual Computing},
 series={VC '21},
 title={Bi-Projection Based Foreground-Aware Omnidirectional Depth Prediction},
 year={2021},
 month={9},
 numpages={6},
}

RIS

TY  - CONF
AU  - Feng, Qi
AU  - Shum, Hubert P. H.
AU  - Morishima, Shigeo
T2  - Proceedings of the 2021 Visual Computing
TI  - Bi-Projection Based Foreground-Aware Omnidirectional Depth Prediction
PY  - 2021
Y1  - 9 2021
ER  - 

Plain Text

Qi Feng, Hubert P. H. Shum and Shigeo Morishima, "Bi-Projection Based Foreground-Aware Omnidirectional Depth Prediction," in VC '21: Proceedings of the 2021 Visual Computing, Sep 2021.

Supporting Grants

Similar Research

Qi Feng, Hubert P. H. Shum and Shigeo Morishima, "360 Depth Estimation in the Wild - The Depth360 Dataset and the SegFuse Network", Proceedings of the 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 2022
Qi Feng, Hubert P. H. Shum and Shigeo Morishima, "Foreground-Aware Dense Depth Estimation for 360 Images", Journal of WSCG - Proceedings of the 2020 International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), 2020
Qi Feng, Hubert P. H. Shum and Shigeo Morishima, "Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation", Proceedings of the 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2023
Li Li, Khalid N. Ismail, Hubert P. H. Shum and Toby P. Breckon, "DurLAR: A High-fidelity 128-Channel LiDAR Dataset with Panoramic Ambientand Reflectivity Imagery for Multi-Modal Autonomous Driving Applications", Proceedings of the 2021 International Conference on 3D Vision (3DV), 2021

 

 

Last updated on 17 February 2024
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