In this paper, we present a large-scale fine-grained dataset using high-resolution images captured from locations worldwide. Compared to existing datasets, our dataset offers a significantly larger size and includes a higher level of detail, making it uniquely suited for fine-grained 3D applications. Notably, our dataset is built using drone-captured aerial imagery, which provides a more accurate perspective for capturing real-world site layouts and architectural structures. By reconstructing environments with these detailed images, our dataset supports applications such as the COLMAP format for Gaussian Splatting and the Structure-from-Motion (SfM) method. It is compatible with widely-used techniques including SLAM, Multi-View Stereo, and Neural Radiance Fields (NeRF), enabling accurate 3D reconstructions and point clouds. This makes it a benchmark for reconstruction and segmentation tasks. The dataset enables seamless integration with multi-modal data, supporting a range of 3D applications, from architectural reconstruction to virtual tourism. Its flexibility promotes innovation, facilitating breakthroughs in 3D modeling and analysis.
View on arXiv@article{zheng2025_2501.06927,
title={ CULTURE3D: Cultural Landmarks and Terrain Dataset for 3D Applications },
author={ Xinyi Zheng and Steve Zhang and Weizhe Lin and Aaron Zhang and Walterio W. Mayol-Cuevas and Junxiao Shen },
journal={arXiv preprint arXiv:2501.06927},
year={ 2025 }
}