关键词: 点云压缩, 点云复原, 向量相似度

Abstract:

Data compression for 3D point cloud suffers from several problems, including difficult to preserve the feature details, over-compress, and difficult to restore a compressed model. To alleviate these problems, CVS (compression based on vector similarity), consisting of a compression algorithm and a restoration algorithm based on vector similarity is proposed. This paper proposes L3A (one length, three angles) for vector similarity metrics. CVS takes a 3D point as a 3D vector connecting its coordinates and origin. According to the reading order of the 3D coordinate points, reference vectors are selected to form sampling areas covering the entire point cloud, and each area is compressed independently. In the sampling area, the least squares surface fitting algorithm is used to perform surface fitting on the point cloud including the curve, the curvature threshold is set to eliminate the coordinate points, and the surface equation parameters are stored for restoration. By controlling the length and angle changing thresholds in the L3A vector similarity, the compression rate of the dense point cloud region is higher than that of the non-dense region, and by controlling the curvature threshold, the compression ratio of the low curvature region is higher than that of the high curvature region. The compression ratio preserves the model’s detail features to the utmost extent. CVS uses the recovery information generated by the compression phase to generate a point cloud to restore the detailed features of the model, making the model features more obvious.

Key words: point cloud compression, point cloud restoration, vector similarity