3D Shape Generation via Variational Autoencoder with Signed Distance Function Relativistic Average Generative Adversarial Network

3D Shape Generation via Variational Autoencoder with Signed Distance Function Relativistic Average Generative Adversarial Network

3D shape generation is widely applied in various industries to create, visualize, and analyse complex data, designs, and simulations. Typically, 3D shape generation uses a large dataset of 3D shapes as the input. This paper proposes a variational autoencoder with a signed distance function relativis...

Olles dieđut

Bibliográfalaš dieđut
Váldodahkkit: Ebenezer Akinyemi Ajayi, Kian Ming Lim, Siew-Chin Chong, Chin Poo Lee
Materiálatiipa: Artihkal
Giella:English
Almmustuhtton: MDPI AG 2023-05-01
Ráidu:Applied Sciences
Fáttát:
3D shape generation
variational autoencoder
generative adversarial network
signed distance function
relativistic average
Liŋkkat:https://www.mdpi.com/2076-3417/13/10/5925

Interneahtta

https://www.mdpi.com/2076-3417/13/10/5925

Geahča maid