Depth grid-based local description for 3D point clouds

With the rapid development and extensive application of next-generation image processing technologies, the manufacturing industry is increasingly adopting intelligent equipment. To meet the demands of high precision and high efficiency production, there has been a growing focus on researching 3D point cloud processing methods that go beyond traditional approaches. A fundamental and crucial challenge in the field of point cloud processing is establishing a point-to-point correspondence mapping between two point clouds, which relies on leveraging the local feature description information inherent in the point cloud. This paper thoroughly investigates novel local description methods based on point cloud processing. It addresses the issue of inadequate descriptive capability and robustness found in existing local description methods. Specifically, this study explores the encoding of point information in the neighborhood space and multi-view projection mapping, proposing a local point cloud description method based on depth grids. This method leverages a local reference frame and establishes a depth grid after obtaining the local reference frame through neighborhood projection and distance weighting. The contribution of neighboring points to the depth of the grid is calculated to obtain the eigenvalues. To enhance efficiency, the calculation of eigenvalues incorporates normalization and multi-view projection techniques. The proposed method is compared and evaluated against various local description methods to verify its effectiveness and accuracy.