@misc{10481/84960,
year = {2023},
month = {7},
url = {https://hdl.handle.net/10481/84960},
abstract = {The paper presents a novel solution to the issue of incomplete regions in 3D meshes obtained through
digitization. Traditional methods for estimating the surface of missing geometry and topology often
yield unrealistic outcomes for intricate surfaces. To overcome this limitation, the paper proposes
a neural network-based approach that generates points in areas where geometric information is
lacking. The method employs 2D inpainting techniques on color images obtained from the original
mesh parameterization and curvature values. The network used in this approach can reconstruct the
curvature image, which then serves as a reference for generating a polygonal surface that closely
resembles the predicted one. The paper’s experiments show that the proposed method effectively fills
complex holes in 3D surfaces with a high degree of naturalness and detail. This paper improves the
previous work in terms of a more in-depth explanation of the different stages of the approach as well
as an extended results section with exhaustive experiments.},
organization = {Spanish Ministry of Science
and Technology under projects PID2020-119478GB-I00},
organization = {TED2021-132702B-C21},
organization = {MCIN/AEI/10.13039/501100
011033},
organization = {European Regional Development Fund (ERDF)},
publisher = {Elsevier},
keywords = {Shape completion},
keywords = {Curvature representation},
keywords = {Parameterization},
keywords = {Inpainting},
title = {Deep learning of curvature features for shape completion},
doi = {10.1016/j.cag.2023.07.007},
author = {Hernández Bautista, Marina and Melero Rus, Francisco Javier},
}