Fast subsampling strategy for point cloud based on novel octree coding

Abstract

Abstract Owing to the continuous expansion in data scale, the calculation, storage, and transmission of 3D data have been plagued by numerous issues. The point cloud data, in particular, often contain duplicated and anomalous points, which can hinder tasks such as measurement. To address this issue, it is crucial to utilize point cloud pre-processing methods that combine subsampling and denoising. These methods help obtain clean, evenly distributed, and compact points to enhance the accuracy of the data. In this study, an efficient point cloud subsampling method is proposed that combines point cloud denoising capabilities. This method can effectively preserve salient features while improving the quality of point cloud data. By constructing the octree structure of the point cloud, the corresponding node code is obtained according to the spatial coordinates of the point cloud, and the feature vector of the node is calculated based on the analysis of covariance. Node feature similarity is introduced to distinguish the node into feature and non-feature nodes, forming the node feature code, and the layer threshold is introduced to filter outliers. Experimental results demonstrate that our proposed algorithm has a time ratio of over four compared to the curvature-based algorithm. Additionally, it exhibits an average grey entropy that is 1.6 × e − 3 lower than that of the random sampling method. And considering both time cost and subsampling effectiveness, proposed algorithm outperforms the state-of-the-art subsampling strategies, such as Approximate Intrinsic Voxel Structure and SampleNet. This approach is effective in removing noise while preserving important features, thereby reducing overall size of the point cloud. The high computational efficiency of our algorithm makes it a valuable reference for fast and precise measurements that require timeliness. It successfully addresses the challenges posed by the continuous expansion of data scale and offers significant advantages over existing subsampling methods. By improving the quality of point cloud data, our algorithm contributes to reducing complexity, enables efficient and accurate measurements.