Enhancing single-view 3D mesh reconstruction with the aid of implicit surface learning

Fahim, George; Amin, Khalid; Zarif, Sameh

doi:10.1016/j.imavis.2022.104377

Cited by 10 publications

(1 citation statement)

References 6 publications

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“…Generative models although not yet used to their full potential to produce engineering designs [ 40 ] but have already proven themselves to be immensely capable of inferring 3D shapes from 2D images. Variational autoencoders (VAEs) [ 41 ] and generative adversarial networks (GANs) [ 42 ] are the two significant types of generative deep convolution neural networks (CNNs) that have been extensively researched to perform generative tasks [ 22 – 31 , 43 , 44 ].…”

Section: Related Workmentioning

confidence: 99%

A cyber-physical system to design 3D models using mixed reality technologies and deep learning for additive manufacturing

Malik¹,

Lhachemi²,

Shorten³

2023

PLoS ONE

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I-nteract is a cyber-physical system that enables real-time interaction with both virtual and real artifacts to design 3D models for additive manufacturing by leveraging mixed-reality technologies. This paper presents novel advances in the development of the interaction platform to generate 3D models using both constructive solid geometry and artificial intelligence. In specific, by taking advantage of the generative capabilities of deep neural networks, the system has been automated to generate 3D models inferred from a single 2D image captured by the user. Furthermore, a novel generative neural architecture, SliceGen, has been proposed and integrated with the system to overcome the limitation of single-type genus 3D model generation imposed by differentiable-rendering-based deep neural architectures. The system also enables the user to adjust the dimensions of the 3D models with respect to their physical workspace. The effectiveness of the system is demonstrated by generating 3D models of furniture (e.g., chairs and tables) and fitting them into the physical space in a mixed reality environment. The presented developmental advances provide a novel and immersive form of interaction to facilitate the inclusion of a consumer into the design process for personal fabrication.

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Section: Related Workmentioning

confidence: 99%

A cyber-physical system to design 3D models using mixed reality technologies and deep learning for additive manufacturing

Malik¹,

Lhachemi²,

Shorten³

2023

PLoS ONE

View full text Add to dashboard Cite

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The use of CNNs in VR/AR/MR/XR: a systematic literature review

Cortes,

Bermejo,

Juiz

2024

Virtual Reality

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This study offers a systematic literature review on the application of Convolutional Neural Networks in Virtual Reality, Augmented Reality, Mixed Reality, and Extended Reality technologies. We categorise these applications into three primary classifications: interaction, where the networks amplify user engagements with virtual and augmented settings; creation, showcasing the networks’ ability to assist in producing high-quality visual representations; and execution, emphasising the optimisation and adaptability of apps across diverse devices and situations. This research serves as a comprehensive guide for academics, researchers, and professionals in immersive technologies, offering profound insights into the cross-disciplinary realm of network applications in these realities. Additionally, we underscore the notable contributions concerning these realities and their intersection with neural networks.

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Deep learning framework-based 3D shape reconstruction of tanks from a single RGB image

Chen

Zhu

Han

et al. 2023

Engineering Applications of Artificial Intelligence

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Enhancing single-view 3D mesh reconstruction with the aid of implicit surface learning

Cited by 10 publications

References 6 publications

A cyber-physical system to design 3D models using mixed reality technologies and deep learning for additive manufacturing

A cyber-physical system to design 3D models using mixed reality technologies and deep learning for additive manufacturing

The use of CNNs in VR/AR/MR/XR: a systematic literature review

Deep learning framework-based 3D shape reconstruction of tanks from a single RGB image

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