Implicit Surface Reconstruction Method Based on Graph Neural Network and Optimized Sampling Strategy
Aiming at the common problems of blurred details and missing local information in neural implicit surface reconstruction using volume rendering technology,this paper proposes an improved implicit surface reconstruction method.First,a graph neural network(GNN)is used to extract features from different target views,and these feature maps are used as supervision information to guide the reconstruction process.Secondly,an optimized Monte Carlo path tracing technology based on scene geometry and lighting characteristics is also introduced.Through an adaptive importance sampling strategy,the light path with the greatest contribution is prioritized for sampling.Finally,the Omnidata pre-trained model is used to extract depth information and normal information to impose additional constraints on the reconstruction process.The results show that compared with existing technologies,this method performs well in improving the accuracy of surface reconstruction and the effect of view rendering.
万方数据
维普
