Generating 3D Voxel designs using advanced Deep Learning techniques-GANs

[Panchadip-128]

This project implements a Generative Adversarial Networks framework (GAN) to generate 3D voxel data. The goal is to train a GAN to produce synthetic 3D voxel-based structures that resemble real-world data, allowing for data augmentation and analysis of generated samples.

Project Overview
This repository contains code for:

Generator: A neural network model that takes a latent vector (noise) as input and generates 3D voxel data.
Discriminator: A neural network model that distinguishes between real voxel data and generated voxel data.
GAN Model: A combination of the generator and discriminator models, trained together in an adversarial setup.
The model is trained on 3D voxel datasets and can generate new voxel structures by learning the underlying data distribution





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[github-actions]

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[Panchadip-128]

@Niketkumardheeryan hi sorry for intervention but pls review as time is less

[Panchadip-128]

@Niketkumardheeryan hi sorry for intervention but pls review as time is less