Multi-view reconstruction method integrating gradient and gaussian process regression

Aiming at the problems that the feature map is sensitive to illumination changes and the reconstruction is incomplete when using depth neural network for multi-view image 3D reconstruction, a multi view reconstruction method integrating gradient and Gaussian process regression is proposed. a multi-view reconstruction method integrating gradient and Gaussian process regression is proposed. Firstly, aiming at the problem that the illumination change affects the extraction of features, a feature extraction network integrating gradient is designed. Through the independent gradient calculation of the image and the convolution neural network is used to extract features based on the gradient and the original image, the influence of gradient information in the feature map is improved and the inhibition of the influence of illumination change factors is enhanced. Secondly, aiming at the problem that the feature extraction step in multi-view reconstruction only focuses on the current view without considering the potential spatial relationship between views, a view feature enhancement module integrating Gaussian process regression algorithm is proposed, which effectively increases the influence of relevant information between views on the multi-view stereo vision reconstruction task and improves the completeness of the multi-view stereo vision reconstruction results. Finally, the contribution of different views to CostVolume is calculated by measuring the degree of matching between the reference image and the adjacent image features, reconstructing CostVolume that conforms to visual perception. Experiments on the DTU and Tanks and Temples datasets show that compared with the mainstream multi-vision stereoscopic vision reconstruction method, the method has a great improvement in the completeness of three-dimensional reconstruction and has good generalization.