An Exploration of Neural Radiance Field Scene Reconstruction: Synthetic, Real-world and Dynamic Scenes
This work addresses 3D reconstruction for computer graphics and vision applications, but it is incremental as it builds on existing NeRF and D-NeRF methods with minor extensions.
The project explored 3D scene reconstruction using Neural Radiance Fields (NeRF) for synthetic and real-world static scenes, leveraging faster training from hash encoding, and extended D-NeRF to handle real-world dynamic scenes, though no specific performance numbers were provided.
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.