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August 4, 2026ACM Computing Surveys1 citationsOpen Access

Neural Radiance Fields for the Real World: A Survey

WXWenhui XiaoRCRemi ChierchiaRCRodrigo Santa Cruz

Key Points

  • This survey aims to review the advancements, applications, and challenges associated with Neural Radiance Fields (NeRFs).
  • Compiled key theoretical advancements and alternative scene representations.
  • Investigated applications in reconstruction and impact on computer vision and robotics.
  • Reviewed essential datasets and toolkits associated with NeRFs.
  • Identified significant gaps in the literature regarding NeRFs.
  • Highlighted emerging challenges and directions for future research.
  • Discussed NeRFs' impact on 3D scene representation and various applications.

Abstract

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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Cite This Study

Xiao et al. (2026) studied this question.

synapsesocial.com/papers/6a7198eec47350ef8e49439chttps://doi.org/10.1145/3837758
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