Advances in Integrating GANs and NeRF for Image Generation and 3D Reconstruction

Shaoyang Qu

doi:10.62051/baev1b20

Authors

Shaoyang Qu

DOI:

https://doi.org/10.62051/baev1b20

Keywords:

Generative Adversarial Networks (GAN); Neural Radiance Fields (NeRF); Image Generation; 3D Reconstruction.

Abstract

This paper investigates recent advancements in integrating Generative Adversarial Networks (GAN) with Neural Radiance Fields (NeRF) for image generation and 3D reconstruction. This integration is pivotal for significantly enhancing the quality of image and 3D scene generation across diverse applications, including medical imaging, virtual reality, animation, and film production. The objective of this study is to summarize and compare existing methods, providing both theoretical and methodological insights. A detailed analysis of various methods is conducted, focusing on their strengths and weaknesses in terms of image quality, multi-view consistency, style migration, and texture transfer. Experimental results across different datasets reveal that while these methods perform effectively within their respective application contexts, which generally face challenges related to high computational resource consumption, training complexity, and the need for extensive multi-view data. Despite these issues, significant progress has been made in enhancing image quality and 3D consistency. This study contributes valuable theoretical insights and offers practical guidance for future research. Future efforts should focus on developing more efficient algorithms, refining training techniques, and exploring applications in medical imaging, virtual reality, animation, and film production to advance image processing and computer vision technologies.

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