Recently, structured-light-based scanning systems have gain in popularity and are capable of modeling entire dense shapes that evolve over time with a single scan (a.k.a. one-shot scan). By projecting a static grid pattern onto the object surface, one-shot shape reconstruction methods can scan moving objects while still maintaining dense reconstruction. However, the amount of 3D data produced by these systems grows rapidly with point cloud of millions of points. As a consequence, effective point cloud compression scheme is required to face the transmission need. In this paper we propose a new approach to compress point cloud by taking advantage of the fact that arithmetic coding can be split into two parts: an encoder that actually produces the compressed bitstream, and a modeler that feeds information into the encoder. In particular, for each position point and normal, we propose to calculate the distribution of probabilities based on their spatial prediction as modeler, while classical point cloud coder mainly focus on the reduction of the prediction residual. Experimental results demonstrate the effectiveness of the proposed method.