3D Reconstruction Based On Image Depth Perception Information

3D Reconstruction is a method that can reconstruct the 3D geometric shape and spatial position information of objects from multiple two-dimensional images.It is one of the research focuses in the field of computer vision.Traditional image 3D reconstruction systems often encounter interference,such as environmental changes during shadowing,shadows,lighting,or occlusion of other objects,which makes it impossible to accurately identify and locate 3D objects.In order to overcome the above common problems,the depth-sensitive information obtained based on the texture characteristics,gradient changes of the reconstructed object and the blur degree of the object can be effectively integrated into the calculation.Therefore,how to accurately use image depth perception information in 3D reconstruction has been a research hotspot in recent years.In this paper,through extensive research on related algorithms and techniques for image saliency detection and depth-sensitive information extraction,the research status of depthsensitive information extraction is classified and summarized.At the same time,a saliency detection algorithm based on depth-sensitive information is proposed.The prominent advantage of the proposed model is that it combines the depth perception information with the twodimensional visual saliency detection model to better present the influence of image depth information.This method proposes a new depth contrast and "spatial center prior" information.The significance test factor is combined with the two-dimensional significance test model.The method is compared with the 7 most advanced saliency detection methods in the public database.The results show that the image depth perception factor does have a very good effect on extracting the saliency foreground in complex scenes.After acquiring image depth perception information,Structure from Motion(SFM)algorithm is used to calculate the position and posture of each camera for three-dimensional reconstruction.According to the data obtained by processing the depth image,feature matching is performed on the multi-view image.The Random Sample Consensus(RANSAC)algorithm removes the matching three-dimensional point cloud,and then triangulates the point cloud data.At the same time,the beam model adjustment is used to optimize the entire model,and the depth-sensitive information is applied to the three-dimensional reconstruction.Experimental analysis of the method proposed in this paper,in order to better evaluate the detection performance of various algorithms,in addition to the typical saliency detection evaluation indicators,this paper provides a new evaluation indicator that combines images from multiple scales Depth information is called MDE(Multi-scale depth edge).Finally,the multi-view image is used as the data source,and the depth-sensitive information of the image is combined to obtain a three-dimensional reconstruction model.The results show that the method in this paper is practical and usable.