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

TL;DR

NeRFBK is a comprehensive new benchmark dataset for evaluating NeRF-based 3D reconstruction algorithms, featuring high-quality, multi-scale indoor and outdoor data with precise camera parameters.

Contribution

The paper introduces NeRFBK, a novel dataset that enables rigorous testing and comparison of NeRF-based 3D reconstruction methods across diverse scenarios.

Findings

Provides high-resolution images and videos for benchmarking

Includes diverse indoor and outdoor scenes

Facilitates evaluation of new NeRF algorithms

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.