Improved 3D reconstruction pipeline for enhancing the quality of 3D model generation from multi-view images.

In this research, we slightly modify the conventional pipeline such that it (1) sets a minimum triangulation angle of 3°, (2) reduces (and possibly minimizes) overall re-projection error by simultaneously optimizing all camera poses and 3D points in the bundle adjustment step, and (3) uses a tiling buffer size of 1024 × 1024, to generate, from a set of 2D images, a detailed 3D models of complex objects. We systematically showcase the versatility of this improved photogrammetric pipeline by applying it to three datasets - a proprietary dataset (money plant) and two publicly available datasets (a statue and an old computer). To the best of our knowledge, while previous research has employed photogrammetry techniques for 3D model generation, none has systematically defined the 3d reconstruction pipeline, with a focus on underlying mathematical concepts, Additionally, there has been a lack of evaluation of how the quality of input 2D images impacts quality of the resulting 3D models. Our study evaluates the entropy and image quality (using BRISQUE scores) of the 3D model generated in relation to the image quality of the set of input 2D images, providing insights into the reliability of the proposed photogrammetric approach. Despite utilizing a low-quality image dataset as input, we demonstrated that we achieved a high-quality 3D model. Furthermore, we observed less differences in image quality when comparing the 3D model with the original 2D images. Overall, our research contributes to both theoretical understanding and practical application of photogrammetry for 3D reconstruction as experimented on three distinct image datasets. As a future research scope, we provide suggestions for possible parallelization of some of the steps of the 3D reconstruction pipeline.