Development of an Efficient 3D Reconstruction Solution from Permissive Open-Source Code

3D reconstruction is one of the main topics in computer vision and is heavily applied for creating virtual environments. Photogrammetry is a technique for obtaining 3D information by mapping objects and scenarios using only images. However, this process can take a long time when using large datasets. In this paper, a permissive open-source pipeline is proposed focusing on robustness, efficiency, and low execution time in batch processing. The permissive license allows commercial use without the need of keeping the code open. We mixed an enhanced structure from motion algorithm and a recurrent multi-view reconstruction. We also use Point Cloud Library for normal estimation, surface reconstruction, and texture mapping. We compared our results with COLMAP and MVE techniques using the DTU MVS dataset and real-world scenarios with our own datasets. The results showed a decrease of 69.4% on average time (compared to the best result of other techniques), but also demonstrated the need for more images to generate a complete reconstructed model.