Research On 3D Reconstruction Of Immovable Objects Based On Image Sequence

At present,the three-dimensional reconstruction of objects based on image sequences is one of the most core and cutting-edge technologies in the field of computer vision.It plays an important role in many fields such as medical research,smart city construction,e-commerce,and new media communication.Compared with other 3D reconstruction methods,the advantages of 3D reconstruction based on image sequences are significant,including portable equipment,easy operation,strong adaptability,and low reconstruction cost.In this paper,objects that are immovable or stationary are selected as reconstruction targets,and two-dimensional image sequences collected from different perspectives are used as raw data for three-dimensional reconstruction.Related algorithms are studied in depth to improve reconstruction efficiency as the main improvement direction.Three-dimensional model of real objects.The main research contents and innovations of the paper are as follows:(1)In order to improve the reconstruction efficiency,an image foreground segmentation algorithm based on Canny edge detection is proposed.The Canny algorithm is used to extract image edge information,and the user marks the object contour at the same time.The contents of the two are compared and the same part is selected as the object contour.Use the flood filling algorithm to obtain the foreground segmentation area,and merge the original image with it to achieve segmentation.Experiments show that compared with other traditional segmentation algorithms,the segmentation effect is remarkable,and it has a high accuracy rate under complex background.Applying this algorithm to 3D reconstruction,preprocessing the image sequence,only extracting the reconstruction target object,removing the background redundant information can achieve the purpose of reducing reconstruction time and improving reconstruction efficiency.(2)Design a set of three-dimensional model reconstruction schemes for immovable objects,and divide the three-dimensional reconstruction process into object geometric model reconstruction and texture mapping.In the aspect of object geometric model reconstruction,first collect the two-dimensional image data of the target object in multiple angles of view,segment the image foreground before reconstruction,remove redundant background information;then perform camera self-calibration,solve the camera parameters,and perform features through the SIFT algorithm Point extraction and match feature points;then use SFM algorithm for sparse point cloud reconstruction,use beam adjustment method to optimize the elimination of error effects,use PMVS algorithm for dense point cloud reconstruction;and finally perform Poisson surface reconstruction.In terms of texture mapping,through the selection of texture images and the elimination of texture seams,the quality of reconstructed model is optimized.The feasibility and accuracy of the design scheme in this paper are verified through experiments.This scheme reduces the time required for reconstruction,and the three-dimensional model reconstruction effect is good.