A Low-Cost 3D Reconstruction System Based on COLMAP and 3D Gaussian Splatting Rendering

DOI

10.2991/978-94-6463-823-3_18How to use a DOI?

Keywords

3D Gaussian Splatting; COLMAP; 3D Reconstruction; Cultural Heritage Digitization; Computer Vision

Abstract

3D reconstruction technologies are increasingly applied in the digitization of cultural heritage. However, traditional photogrammetry methods often involve high costs, while deep learning approaches such as Neural Radiance Fields (NeRF) require intensive computation, making it difficult to balance reconstruction accuracy with efficiency. This study proposes a low-cost 3D reconstruction system based on COLMAP and 3D Gaussian Splatting (3DGS) rendering. Using a custom-built dataset of cultural artifacts, the system first applies COLMAP for sparse point cloud reconstruction and camera pose estimation, followed by 3DGS rendering. The resulting models are compatible with platforms such as Unity for downstream applications. As indicated by quantitative evaluation, the system achieves an average PSNR of 22.06 and SSIM of 0.80, with rendering completed in under 40 min, demonstrating practical performance in balancing visual fidelity and computational efficiency. Compared with traditional methods, the system offers a feasible and cost-effective solution for museum digital exhibitions and scientific instrument preservation.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kaiyuan Lu
PY  - 2025
DA  - 2025/08/31
TI  - A Low-Cost 3D Reconstruction System Based on COLMAP and 3D Gaussian Splatting Rendering
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 200
EP  - 206
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_18
DO  - 10.2991/978-94-6463-823-3_18
ID  - Lu2025
ER  -