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An Exploration of Neural Radiance Field Scene Reconstruction: Synthetic, Real-world and Dynamic Scenes (2210.12268v1)

Published 21 Oct 2022 in cs.CV and cs.AI

Abstract: 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.

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Authors (4)
  1. Benedict Quartey (8 papers)
  2. Tuluhan Akbulut (3 papers)
  3. Wasiwasi Mgonzo (1 paper)
  4. Zheng Xin Yong (4 papers)

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