scholarly journals The Effects of Changing Projection Geometry on Perception of 3D Objects on and Around Tabletops

2016 ◽  
Vol 23 (2) ◽  
pp. 1-54 ◽  
Author(s):  
Miguel A. Nacenta ◽  
Mark Hancock ◽  
Carl Gutwin ◽  
Sheelagh Carpendale
Keyword(s):  
3D Objects ◽  
Projection Geometry

Recognition of partially occluded 3D objects

1989 ◽  
Vol 136 (2) ◽  
pp. 124
Author(s):  
Ming-Hong Chan ◽  
Hung-Tat Tsui
Keyword(s):  
3D Objects ◽  
Partially Occluded

scholarly journals 3D-printing on textiles – an investigation on adhesion properties of the produced composite materials

2021 ◽  
Vol 28 (6) ◽  
Author(s):  
Maryna Gorlachova ◽  
Boris Mahltig
Keyword(s):  
3D Printing ◽  
Adhesive Properties ◽  
Adhesion Properties ◽  
Practical Applications ◽  
3D Print ◽  
3D Objects ◽  
Textile Fabrics ◽  
Polylactide Acid ◽  
Polyamide 6.6 ◽  
Textile Sector

AbstractThe actual paper is related to adhesive properties of 3D objects printed on cotton textile fabrics. For practical applications of 3D prints in the textile sector, the adhesion of the printed object on the textile substrate is an important issue. In the current study, two different types of polymers are printed on cotton – polylactide acid (PLA) and polyamide 6.6 (Nylon). Altogether six cotton fabrics differing in structure, weight and thickness are evaluated. Also, the effect of washing and enzymatic desizing is investigated. For printing parameters, best results are gained for elevated process temperatures, intermediate printing speed and low Z-distance between printing head and substrate. Also, a textile treatment by washing and desizing can improve the adhesion of an afterwards applied 3D print. The presented results are quite useful for future developments of 3D printing applications on textile substrates, e.g. to implement new decorative features or protective functions.

White Light Multiplexed Holograms of 3D Objects

1978 ◽  
Vol 7 (3) ◽  
pp. 47-51
Author(s):  
P. C. Mehta ◽  
Crander Bhan ◽  
Pritam Lal ◽  
Dbvender Mohan
Keyword(s):  
White Light ◽  
3D Objects

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

Sign in / Sign up

Export Citation Format

Share Document