Self-similarity based compression of point set surfaces with application to ray tracing
ABSTRACT Many real-world, scanned surfaces contain repetitive structures, like bumps, ridges, creases, and so on. We present a compression technique that exploits self-similarity within a point-sampled surface. Our method replaces similar surface patches with an instance of a representative patch. We use a concise shape descriptor to identify and cluster similar patches. Decoding is achieved through simple instancing of the representative patches. Encoding is efficient, and can be applied to large data sets consisting of millions of points. Moreover, our technique offers random access to the compressed data, making it applicable to ray tracing, and easily allows for storing additional point attributes, like normals.
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ABSTRACT: This paper presents a robust spatial watermarking scheme for D point cloud models using self-similarity partition. In the new scheme, D point cloud model is uniquely partitioned into patches using octree structure and PCA preprocessing. Then a concise similarity measurement is designed to identify and cluster similar patches to similar patch chains and the codebook is formed. By altering local vector direction of certain points of each patch, the watermark bits are embedded into the average local vector direction of every similar patch. The watermark can be extracted based on three keys, the known model center, the principal object axis of the original model, and the codebook. Furthermore, to make the proposed watermarking scheme robust against various forms of attack while preserving the visual quality of the models our scheme repeatedly embeds a bit of the watermark in every self-similarity clustered patches. Experimental results show that the proposed watermarking scheme has good imperceptibility and performs well under common 3D watermarking attacks such as uniform affine transformations, simplification, resample, cropping, additive noise.Proceedings of the IEEE International Conference on Wireless Communications, Networking and Information Security, WCNIS 2010, 25-27 June 2010, Beijing, China; 01/2010
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ABSTRACT: This study presents a new high-capacity spatial steganographic scheme for 3D point cloud models using a Self-Similarity Position Matching (SSPM) procedure. The new scheme partitions the 3D point cloud model to patches, clusters patches into similarity patch chains using self-similarity measures and generates the codebook. The representative patches and similar patches are then taken from the codebook for every similar patch chain as the reference patches and the message patches. Finally, every message point in the similar message patches which has the point-to-point correspondence with a certain reference point in the reference patch can embed at least four bits using the proposed SSPM, which embeds information by shifting the message point from current point to the corresponding embedding position that is computed over virtual sphere with the reference point as the center. Experimental results show that the proposed technique is secure, has high capacity and low distortion and is robust against uniform affine transformations such as transformation, rotation, scaling. In addition, a concise shape description and a similarity measures are devoted to improving performance for forming codebook and constructing point traversal. The technique can be considered as a side-match steganography and has proven to be a feasible alternative to other steganographic schemes for 3D point cloud model.Information Technology Journal. 01/2010;