Progressive Compression of Point-Sampled Models

Abstract

decomposition of the point set and thus easily allows for progressive decoding. Our method is generic in the sense that it can handle arbitrary point attributes using attribute-specific coding operations. Furthermore, no resampling of the model is needed and thus we do not introduce additional smoothing artifacts. We provide coding operators for the point position, normal and color. Particularly, by transforming the point positions into a local reference frame, we exploit the fact that all point samples are living on a surface. Our framework enables for compressing both geometry and appearance of the model in a unified manner. We show the performance of our framework on a diversity of point-based models.