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Neural Radiance Fields for the Real World: A Survey

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arxiv 2501.13104 v3 pith:24HKTWE3 submitted 2025-01-22 cs.CV cs.GR

classification cs.CVcs.GR
keywords applicationschallengesfieldsnerfsscenesurveyneuralradiance
verification ladder T0 review T1 audit T2 compute T3 formal
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Neural Radiance Fields (NeRFs) have remodeled 3D scene representation since release. NeRFs can effectively reconstruct complex 3D scenes from 2D images, advancing different fields and applications such as scene understanding, 3D content generation, and robotics. Despite significant research progress, a thorough review of recent innovations, applications, and challenges is lacking. This survey compiles key theoretical advancements and alternative scene representations and investigates emerging challenges. It further explores applications on reconstruction, highlights NeRFs' impact on computer vision and robotics, and reviews essential datasets and toolkits. By identifying gaps in the literature, this survey discusses open challenges and offers directions for future research.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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