Research On Data Types Conversion And Data Simplification Algorithm In 3D Data Compression

With the development of 3D reconstruction algorithm and the optimization of data acquisition and processing equipment,the application of 3D reconstruction technology in product shape detection,industrial rapid prototyping,computer-aided design,film and television entertainment,virtual reality,target recognition,3D printing and other fields is becoming more and more mature.With the increasing demand of industry application and the improvement of data precision,the amount of 3D data generated by 3D reconstruction has increased dramatically in recent years,which become new challenges of hardware storage and later processing.This paper studies the compression of 3D data from two aspects: the types of 3D data conversion and data simplification.The detailed contents of the research are as follows:1.A 3D data compression algorithm based on virtual orthogonal structured light is proposed,which maps 3D data to a 2D gray image for storage,so as to save data memory.The 3D data is mapped into two sinusoidal stripes with phase shift difference of ?2 through the virtual orthogonal structured light model during the processes of coding,,and then the compound stripes are formed by orthogonal methd.The corresponding fringe image is separated by frequency domain filtering during the decoding process,and the phase information is computed to get 3D data.Compared with the traditional virtual structured light 3D data coding algorithm,the orthogonal composite coding method has an advantage over the storing fringe patterns method by three channels(R,G and B)of color image,and the storage amount of gray image data is reduced by 2/3.2.A 3D data compression algorithm based on complex local feature simplification is proposed.Firstly,the K-Neighborhood structure is established by using the nearest neighbor algorithm,and K-Neighborhood coordinates of any point in the point cloud are obtained.Then,the feature parameters such as curvature factor of the point cloud are calculated by using the operator descripted by neighborhood variance features,and the strong feature points of the point cloud are extracted by the threshold of the feature parameters.Finally,the cuboid grid method is used to simplify the weak feature points the non feature points of point cloud.Compared with the traditional bounding box method,the proposed algorithm preserves the contour completely in the feature area with large curvature,and evenly preserves the non feature points in the non feature area with small curvature.Experimental results show that the proposed algorithm can retain the local complex features of 3D point cloud data well.Compared with the traditional 3D data simplification algorithm,the geometric error of this algorithm is the smallest.The experiment shows that with the increase of the reduction rate,the error of the proposed algorithm change slightly.