Research And Application Of 3d Geometric Feature Recognition Method Based On Single Image

3D geometric feature recognition technology based on image has always been one of the important research contents in the field of computational vision.The research of 3D feature recognition has also developed rapidly and been widely used in industrial automation production detection,virtual reality,face recognition and other fields,with the rapid development of image processing and 3D reconstruction technology.Therefore,the research of 3D feature recognition technology based on single image,which has the advantages of high efficiency,convenience and accuracy,has important practical significance for our production and life.Most of the image-based 3D feature recognition technologies need to input multiple images at present,and image registration between multiple images is required.The process is complex and the amount of calculation is large.Moreover,if the characteristic space scale of the shape index is not properly chosen for a point in the discrete domain,the shape index calculated directly from the 3D geometric data may be larger errors.The traditional technology did not take into account the influence of external environment on the image of the object,so the recognition result is not universal.In addition,most of the recognition technologies perform better on triangular mesh data,but the effect of point cloud data without clear grid structure is not good enough.Therefore,its universal property needs to be further improved.In view of the above problems,this paper has carried out specific research,especially in improving the accuracy of geometric description operator.The main work and innovation of this paper are as follows:(1)Initial input data is a single 2D gray image.The traditional method of extracting 3D geometric information based on single image is improved.Firstly,the direction parameters of the light source are estimated by using optical knowledge.Secondly,3D surface reconstruction of the target object based on Lambert reflection model and Newton iteration is carried out.Finally,the basic features,such as the Gauss curvature,the mean curvature and the principal curvature,representing the local characteristics of the surface are extracted.(2)The appropriate characteristic space scale should be selected,when calculating the shape index of point cloud data.The scale space equation of 3D curve in 3D solid space is deduced by using Gauss filtering and infinitesimal analysis.Further,the equation is extended to the surface in 3D space.An error function for the average displacement of points on a surface is established,and the characteristic scale is defined as the maximum normalized distance of points,thus the shape index is correlated with the feature scale.(3)An order of magnitude,curvature measure,is introduced for geometric feature recognition.It improves the accuracy of geometric feature recognition and obtains information about the underlying geometric shape.The 3D geometric features are systematically analyzed and classified based on shape index,Combining the above research contents.(4)The proposed geometric feature recognition method is applied to workpiece recognition for related research and experiments.In the process of recognition,the representative image of workpiece types is used.The classification method with strong generalization ability,namely support vector machine,is selected to train and recognize the selected workpiece sample image in turn.3D geometric feature recognition based on single image shows high efficiency and accuracy.