Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00364661" target="_blank" >RIV/68407700:21230/21:00364661 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/3DV53792.2021.00033" target="_blank" >https://doi.org/10.1109/3DV53792.2021.00033</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/3DV53792.2021.00033" target="_blank" >10.1109/3DV53792.2021.00033</a>
Alternative languages
Result language
angličtina
Original language name
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss
Original language description
Deep neural networks have recently thrived on single image depth estimation. That being said, current developments on this topic highlight an apparent compromise between accuracy and network size. This work proposes an accurate and lightweight framework for monocular depth estimation based on a self-attention mechanism stemming from salient point detection. Specifically, we utilize a sparse set of keypoints to train a FuSaNet model that consists of two major components: Fusion-Net and Saliency-Net. In addition, we introduce a normalized Hessian loss term invariant to scaling and shear along the depth direction, which is shown to substantially improve the accuracy. The proposed method achieves state-of-the-art results on NYU-Depth-v2 and KITTI while using 3.1-38.4 times smaller model in terms of the number of parameters than baseline approaches. Experiments on the SUN-RGBD further demonstrate the generalizability of the proposed method.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
3DV 2021: Proceedings of the International Conference on 3D Vision
ISBN
978-1-6654-2688-6
ISSN
2378-3826
e-ISSN
2475-7888
Number of pages
11
Pages from-to
228-238
Publisher name
IEEE Computer Soc.
Place of publication
Los Alamitos, CA
Event location
Virtual
Event date
Dec 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000786496000023