This survey aims to investigate fundamental deep learning (DL) based 3D reconstruction techniques that produce photo-realistic 3D models and scenes, highlighting Neural Radiance Fields (NeRFs), Latent Diffusion Models (LDM), and 3D Gaussian Splatting. We dissect the underlying algorithms, evaluate their strengths and tradeoffs, and project future research trajectories in this rapidly evolving field. We provide a comprehensive overview of the fundamental in DL-driven 3D scene reconstruction, offering insights into their potential applications and limitations.

翻译：本综述旨在研究基于深度学习（DL）的、能够生成逼真三维模型与场景的基础三维重建技术，重点探讨神经辐射场（NeRF）、潜在扩散模型（LDM）与三维高斯溅射（3D Gaussian Splatting）。我们剖析其底层算法，评估其优势与权衡，并展望这一快速发展领域的未来研究方向。本文全面概述了深度学习驱动的三维场景重建基础技术，深入探讨了其潜在应用与局限性。