Point cloud enhancement optimization and high-fidelity texture reconstruction methods for air material via fusion of 3D scanning and neural rendering

In order to realize the digital management and manufacturing of air material, the methods of 3D point cloud enhancement optimization and high-fidelity texture reconstruction in entity digitization of air material based on the fusion of 3D scanning and neural rendering are proposed. In this paper, an automatic data acquisition system is designed and built, and the acquired training images are enhanced. Then the Yolov8 segmentation model and CascadePSP boundary refinement model are combined for training to realize foreground segmentation and background removal of air material images. In this paper, a 3D point cloud enhancement optimization method is proposed, which combines the point cloud obtained by binocular structured light scanner and the point cloud reconstructed by Colmap algorithm for registration and fusion. By combining the coarse registration of PointNetLK network with the fine registration of ICP algorithm, a more complete and high-quality dense point cloud is generated, and the effects of illumination conditions and image quantity on point cloud reconstruction is studied experimentally. Using the optimized point cloud model and random perspective enhancement (RPE) method, the neural rendering model NeuS is improved, which improves the 3D surface texture reconstruction quality and the sight extrapolation effect. In order to verify the effectiveness of the proposed method, comparison and ablation experiments are carried out in this paper. The experimental results show that point cloud enhancement optimization and RPE have a good effect on improving the model, and the model performance has been greatly improved.