Efficient Representation Of Point Cloud By Using TSLVQ For Augmented Reality

Virtual Reality(VR)and Augmented Reality(AR)are announced as the next digital technologies that,after those of the Web and Mobile,will revolutionize our modes of communication.VR and AR are therefore at the heart of many researches,and in particular those in point cloud compression and simplification.Recent advances in timevarying 3D data acquisition techniques have made video-rate point clouds capture available.These point clouds record global rigid deformation and local non-rigid deformations.Millions of point clouds are full of a lot of redundancy,which causes a lot of problems to storage,transmission and some other digital process,especially on the internet,filled with billions of photos.Indeed,high quality compression schemes are rather necessary.In the augmented reality,3D dynamic contents rendering technology including point cloud,3D mesh or light field,which are quite different from traditional video technology and MPEG coding is not suitable for AR(but proper for VR).Concerning equipment precision becoming higher and higher,3D point cloud data becoming larger and larger,this paper concentrates on the techniques of point cloud compression and simplification.Besides,the efficient representation of static point cloud algorithm comes true in Matlab and in the platform Visual Studio another efficient representation of dynamic contents comes true by utilizing C++.The main work of this paper is divided into three parts,as follows:First,concerning the raw point cloud data acquisition,data format,this paper assembles some related work.For three rendering technique categories in augmented reality,this paper summarizes some existing works.And in the last section,give the static 3D scene and dynamic 3D scene rendering created in Blender for augmented reality.Second,concerning the problems that in point cloud with a lot of temporal redundancy,this paper presented a novel lossy compression approach based on treeStructure Lattice Vector Quantization for static point cloud.The proposed approach utilizes the hierarchical packing of embed truncated lattices,which can solve the problem of representation inefficiency.It allows for predefining max level to control coding complexity and coding precision to meet the real-time rendering requirement.Experimental results show that the octree decomposition has obvious advantages in 3D object real-time representation comparing with the existing gridding lossy compression,guaranteeing 3D model reconstruction precision.Third,concerning dynamic content real-time rendering inefficiency,this paper proposes a novel point cloud stream compression algorithm based on tree structure lattice vector quantization.On the geometry,first,based on the static point cloud rendering algorithm in this paper,parallelly process those two consecutive point cloud frames.And it results as two octrees and the corresponding binary sequences.Second,by coding the structure differentials between consecutive frames,reduce the spatial and temporal redundancy.On the color,utilize k-means nearby method to predict color value in new frame.Furthermore,it allows for predefining the size of the cube in the deepest level to control coding complexity and coding precision to meet the real-time rendering requirement.Besides,analyze the compression result in detailed block,compression ratio and consuming times in the point cloud datasets in different resolutions.Experiment results reveal that this compression method is quite promising in augmented reality,and prepared for next step,3D model.