Reconstruction of Building LIDAR Point Cloud Based on Geometric Primitive Constrained Optimization

This study proposes a 3D reconstruction method for LIDAR building point clouds using geometric primitive constrained optimization. It addresses challenges such as low accuracy, high complexity, and slow modeling. This new algorithm studies the reconstruction of point clouds at the level of geometric primitives and is an incremental joint optimization method based on the GPU rendering pipeline. Firstly, the building point cloud collected by the LIDAR laser scanner was preprocessed, and an initial building mesh model was constructed by the fast triangulation method. Secondly, based on the geometric characteristics of the building, geometric primitive constrained optimization rules were generated to optimize the initial mesh model (regular surface optimization, basis spline surface optimization, junction area optimization, etc.). And a view-dependent parallel algorithm was designed to optimize the calculation. Finally, the effectiveness of this approach was validated by comparing and analyzing the experimental results of different buildings’ point cloud data. This algorithm does not require data training and is suitable for outdoor surveying and mapping engineering operations. It has good controllability and adaptability, and the entire pipeline is interpretable. The obtained results can be used for serious applications, such as Building Information Modeling (BIM), Computer-Aided Design (CAD), etc.