| dc.description.abstract | In today’s digital era, 3D scanning and reconstruction technologies have become pivotal in various fields such as autonomous driving, precision engineering, and cultural heritage preservation, allowing us to accurately create digital model of physical objects for analysis and virtual interactions. However, these advanced technologies often encounter obstruction from various types of noise during implementation, which from misidentification of image features, environmental conditions, or the complexity of object surfaces, thereby causing loss of object features in the 3D reconstruction process and posing significant challenges to the reconstruction and description of object features. Therefore, developing efficient methods for noise identification and reduction becomes particularly important to enhance the accuracy and reliability of 3D scanning and reconstruction technologies.
This study proposes an innovative 3D point cloud reconstruction method based on Structure from Motion (SfM) and entropy calculation. By introducing entropy calculation in the image processing phase, this method effectively enhances the richness of epipolar geometry and point clouds numbers. Additionally, the improved density entropy Bundle Adjustment allows point clouds to be more detailed, presenting a more refined texture in the 3D point cloud model. Finally, the resulting point cloud model is integrated into the Unreal Engine, enabling real-time updates and highly realistic visual effects, displaying the potential applications and innovative value of this research in the field of 3D reconstruction.
Experimental results show that this method significantly outperforms existing SfM methods in terms of point cloud density and detail richness. Although the computation time is longer, the generated point cloud model has clear advantages in density and detail, which is especially beneficial for applications requiring detailed 3D reconstruction. By applying this method to images captured by industrial cameras, it successfully creates a digital twin model and integrates it into the Unreal Engine for real-time monitoring. This not only demonstrates the practical application potential of the proposed method but also highlights its innovative value in the fields of digital twins and virtual reality. | en_US |