A New 3D Representation and Compression Algorithm for Non-Rigid Moving Objects using Affine-Octree

This paper presents a new 3D representation for non-rigid objects using motion vectors between two consecutive frames. Our method relies on an Octree to recursively partition the object into smaller parts for which a small number of motion parameters can accurately represent that portion of the object. The partitioning continues as long as the respective motion parameters are insufficiently accurate to describe the object. Unlike other Octree methods, our method employs an affine transformation for the motion description part, which greatly reduces the storage. Finally, an adaptive thresholding, a singular value decomposition for dealing with singularities, and a quantization and arithmetic coding further enhance our proposed method by increasing the compression while maintaining very good signal-noise ratio. Compared with other methods like trilinear interpolation or Principle Component Analysis (PCA) based algorithm, the Affine-Octree method is easy to compute and highly compact. As the results demonstrate, our method has a better performance in terms of compression ratio and PSNR, while it remains simple

A compact representation for 3D animation using octrees and affine transformation

Title from PDF of title page (University of Missouri--Columbia, viewed on March 10, 2010).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Guilherme DeSouza.Vita.M.S. University of Missouri--Columbia 2009.[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] We present a new and compact 3D representation for non-rigid objects using motion vectors between two consecutive frames. Our method relies on an Octree to recursively partition the object into smaller parts. Each part is then assigned a small number of motion parameters that can accurately represent that portion of the object. The partitioning continues as long as the respective motion parameters are insufficiently accurate to describe the object. Our method employs an affine transformation as the motion vectors. A technique using adaptive thresholding, singular value decomposition for dealing with singularities, and a quantization and arithmetic coding further enhance our proposed method by increasing the compression while maintaining very good signal-noise ratio. Besides the work we have done for synthetic data (animation), we also challenge a much more difficult problem - the motion representation for real data (cloud of points), where the correspondence is unknown. We applied Iterative Closest Points (ICP) algorithm for computing a pseudo correspondence, combined with an Octree structure to deal with the non-rigidity that ICP can not capture. About the motion vectors, we still use the affine transformation as we did for animation data. Even though our result for this part is not strong, we give a detailed analysis for the failure and proposed several possible solutions.Includes bibliographical reference