Efficient rate-distortion compression of dynamic point cloud for grid-pattern-based 3D scanning systems

Abstract

Recently 3D scanning systems are capable of modeling entire dense shapes that evolve over time with a single scan (a.k.a. one-shot scan). In particular, structured-light-based systems have emerged as one-shot shape reconstruction methods that project a static grid pattern onto the object surface. This pattern allows the scanning of moving objects while still maintaining dense reconstruction. One-shot scanning systems are then capable of producing 3D+t (a.k.a. 4D) spatio-temporal models with millions of points. As a consequence, effective 4D geometry compression schemes are required to face the need to store or transmit the huge amount of data, in addition to classical static 3D data. In this paper, we propose a 4D spatiotemporal rate-distortion (RD) optimized point cloud encoder via a curve-based representation of the point cloud, particularly well-suited for one-shot scanning systems. The object surface is naturally sampled in a series of curves, due to the grid pattern. This motivates our choice to leverage a curve-based representation to remove the spatial and temporal correlation of the sampled point along the scanning directions through a competitive-based predictive encoder that includes different spatio-temporal prediction modes through an RD cost computation control. Experimental results show the significant gain obtained with the proposed method.