Image augment self-supervised network for hand-drawn sketch 3D reconstruction

Yang Ma; Dehui Kong; Jinghua Li; Baocai Yin

doi:10.1117/12.3005096

9 October 2023 Image augment self-supervised network for hand-drawn sketch 3D reconstruction

Yang Ma, Dehui Kong, Jinghua Li, Baocai Yin

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127912B (2023) https://doi.org/10.1117/12.3005096
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

3D reconstruction from single hand-drawn sketches can be considered as a task of single-view reconstruction, which faces great challenge of lifting the dimension of the geometric representation of objects. Most of the reconstruction networks are based on deep learning technology with supervised training which are used to suffer from dataset labeling, while selfsupervised sketch-based 3D reconstruction remains challenging. In this paper, we propose a self-supervised 3D reconstruction network for hand-drawn sketch (IASSReNet), which introduces image information as an auxiliary to address the ambiguity and sparsity of sketch. In order to obtain image information, an image generator is firstly designed to provide augmented information for the reconstruction through a sketch feature enhancement module. To integrate the information from sketch and image, we use a spatially corresponding feature transfer module to fuse their feature. Finally, silhouettes are obtained from the predicted 3D mesh, and similarity constraints are applied to the sketch contour to perform self-supervised training on the network. Experimental results on multiple datasets show that our method outperforms other unsupervised methods and is competitive with some supervised methods.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yang Ma, Dehui Kong, Jinghua Li, and Baocai Yin "Image augment self-supervised network for hand-drawn sketch 3D reconstruction", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127912B (9 October 2023); https://doi.org/10.1117/12.3005096

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

3D modeling

Image enhancement

Education and training

3D image reconstruction

Feature extraction

Image processing

Modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years