Point set surface compression based on shape pattern analysis

In this work we propose an efficient algorithm for progressive point set surface compression based on shape pattern analysis. The 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 into a 2D domain and regularly resampled. After the resampling, each patch can be described as a height map. Using the height maps, we do the similarity analysis between patches. The patches which have the similar shape are classified into the same cluster, called a shape pattern. For patches in the same shape pattern, a representative patch is computed; then each patch can be represented as the representative patch plus an error correction. When decoding, the profile of the model can be quickly reconstructed using the representative patches and transformation parameters. Then with the decoding of the error image, the model can be gradually refined, implementing progressive compression of 3D point-based models.