NERFBK: A High-Quality Benchmark for NERF-Based 3D Reconstruction

Ali Karami, Simone Rigon, Gabriele Mazzacca, Ziyang Yan, Fabio Remondino
6/9/2023
cs.CVcs.AIcs.GR

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Abstract

This paper introduces a new real and synthetic dataset called NeRFBK specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. High-quality 3D reconstruction has significant potential in various fields, and advancements in image-based algorithms make it essential to evaluate new advanced techniques. However, gathering diverse data with precise ground truth is challenging and may not encompass all relevant applications. The NeRFBK dataset addresses this issue by providing multi-scale, indoor and outdoor datasets with high-resolution images and videos and camera parameters for testing and comparing NeRF-based algorithms. This paper presents the design and creation of the NeRFBK benchmark, various examples and application scenarios, and highlights its potential for advancing the field of 3D reconstruction.

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Cite this paper

@article{karami2023nerfbk,
  title  = {NERFBK: A High-Quality Benchmark for NERF-Based 3D Reconstruction},
  author = {Ali Karami and Simone Rigon and Gabriele Mazzacca and Ziyang Yan and Fabio Remondino},
  year   = {2023},
  eprint = {2306.06300},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.06300v2}
}

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