After the point cloud data of a circular hole is meshed, jagged edges may appear and the profile may become stretched or deformed. This is related to the geometric characteristics of annular structures, which naturally contain concave surfaces and boundaries. If the point cloud density along the circular hole boundary is insufficient and scanning noise exists on the inner wall, the reconstruction algorithm may easily confuse the inner and outer regions of the hole during meshing, resulting in incorrect triangular facet connections.

The completeness of data in thin-walled areas largely determines whether an inspection or reverse engineering project can progress smoothly. By making full use of the raw point cloud data captured by the scanner, 3DMatrix_Gen2 improves the model reconstruction quality of thin-walled workpieces and helps engineers obtain continuous, stable, and reliable 3D data more quickly.

In precision component applications, many machining tool marks, casting micro-depressions, and surface wear traces are clearly discernible to the naked eye. If software processing is inadequate, these subtle microscopic features can easily be misidentified as noise and filtered out. Even when the scanner has already captured valid raw point cloud data, the final generated model may still lose critical features.

The combination of AI enhancement and the reconstruction of native data through a local small model is one of the core aspects of the 3DMatrix_Gen2 upgrade. This version will continue to evolve, achieving a breakthrough in model packaging speed, with overall speeds generally increased by 5–10 times. This will substantially shorten the overall modeling cycle, further connect the entire workflow from high-precision scanning to high-efficiency model generation, and continuously unlock the productivity of industrial 3D digitization.