Compression Of Point-Based3D Models Based On Shape Analysis

With the development of3D scanning and modeling technologies, three dimensional models have become more and more widely used, as a new type of emerging digital media. They have found applications in many fields such as video gaming, movie special effects, computer simulation, autonomous navigation, industrial inspection, reverse engineering, medical diagnosis and digital cultural protection. As a result, increasingly more point set surfaces data are being produced, which give rise to a growing demand on effective compression schemes to efficiently utilize the storage, the processing and/or the bandwidth resources. Hence,3D model compression has become a hot topic in the past two decades.This thesis makes a comprehensive study of the existing work on point-based3D model compression. Based on the problems found in the previous works, we introduce the3D shape analysis technology into the field of3D model compression. Specially, with the help of surface patch segmentation and symmetry analysis, we propose two compression schemes for3D point-based models. The contributions of the thesis mainly include:1. Compression algorithm based on shape pattern analysisThe algorithm proceeds as follows. First, the model surface is segmented into square patches according to the principal directions of the surfel. Then, the square patch is parameterized onto a2D domain and regularly resampled. After resampling, the patch can be described as a height map. Using the height maps, we do the self-similarity analysis between patches. The patches which have the similar shape are classified into one cluster, called a shape pattern. For the patches in a same shape pattern, a representative patch is computed; then the patch can be represented as the representative patch plus the error correction. When decoding, the profile of the model can be quickly constructed using the representative patches and transformation parameters. Then with the decoding of the error image, the model can be refined little by little, thus implementing progressive compression of3D point-based models.2. Symmetry detection algorithm based on iteratively re-weighted principal component analysis (PCA)The iteratively re-weighted PCA process works as follows. An initial approximate symmetry plane is computed through a weighted PCA process, using the area of each surfel as its weight. Thereafter, the approximate symmetry plane is refined iteratively. In each iteration, we firstly update each surfel’s weight based on a distance metric at that surfel, and secondly conduct the weighted PCA to refine the approximate symmetry plane. For each approximate symmetry plane, an associated symmetry metric is computed. The iteration will stop to give the final approximate symmetry plane when the new symmetry plane and the previous one are closely enough or the number of iterations goes beyond a threshold.3. Compression algorithm based on symmetry analysisThe algorithm is suitable for progressive compression of generic3D point-based models based on planar reflective symmetry analysis. To the best of our knowledge, this work pioneers in analyzing and utilizing high-level symmetry information for efficient encoding of point-based3D models. Given a point-based model, the proposed algorithm identifies the primary symmetry plane and, based on which, determines three orthogonal projection planes. By analyzing the model surface’s projections on these planes, the surface is segmented into portions which are classified into symmetric height fields, non-symmetric height fields, and the remaining surface portion. Symmetry based prediction and embedded zerotree wavelet coders are employed for compact coding of the former two types, and octree-based encoder is employed for the last type of portion. As a result, we have made a point-based3D model encoder that achieves generic applicability and excellent rate-distortion performance at the same time, as demonstrated by experiments.