Research on the Simplification Method of 3D Point Cloud

Aiming at the problems of large amount of point cloud data, high density, and high requirements for point cloud simplification in knee joint deformity correction surgery, this paper proposes a point cloud data simplification algorithm that combines octree encoding with distance-based downsampling technology. The algorithm first uses the octree algorithm to segment the original point cloud into multiple cubic subregions with uniform edge lengths, and selects the point closest to the centroid in each subregion. Through K-neighborhood search and distance threshold setting, the point cloud data is divided into regions. Finally, by integrating various optimization algorithms, the knee joint point cloud data is effectively simplified. This algorithm not only improves the precision of data processing but also optimizes the data processing workflow, providing accurate point cloud data support for knee joint correction surgery. Experiments have proven that this method can maintain relatively high simplification speed while retaining key features, providing an effective way for point cloud data processing in knee joint deformity correction surgery.