  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.
