  This project presents an exploration into 3D scene reconstruction of
synthetic and real-world scenes using Neural Radiance Field (NeRF) approaches.
We primarily take advantage of the reduction in training and rendering time of
neural graphic primitives multi-resolution hash encoding, to reconstruct static
video game scenes and real-world scenes, comparing and observing reconstruction
detail and limitations. Additionally, we explore dynamic scene reconstruction
using Neural Radiance Fields for Dynamic Scenes(D-NeRF). Finally, we extend the
implementation of D-NeRF, originally constrained to handle synthetic scenes to
also handle real-world dynamic scenes.
