NERFBK: A High-Quality Benchmark for NERF-Based 3D Reconstruction
This provides a standardized benchmark for researchers in 3D reconstruction to test and compare algorithms, though it is incremental as it builds on existing NeRF methodology.
The paper tackles the challenge of evaluating NeRF-based 3D reconstruction algorithms by introducing the NeRFBK dataset, which provides diverse real and synthetic data with precise ground truth, including multi-scale indoor/outdoor scenes with high-resolution images and camera parameters.
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.