Aiming at the problem of matching distortion caused by structural occlusion， field of view constraints， and stitching errors during point cloud reconstructed， a multi-level filter network （MulFNet） is proposed to achieve single-shot scanning point clouds for low-overlap registration. Firstly， the multi-level features of the point clouds are extracted through the feature pyramid coding network to obtain semantic information at different scales， and the attention module and the location module are embedded to enhance the feature significance； secondly， the multi-level features are filtered based on the multi-scale consistency voting mechanism， outliers are screened out and prominent features of the point clouds are retained to obtain the initial correspondence； and finally， the initial corresponding nodes are adaptively grouped based on the geometric relationships， and weighted estimation conversion is performed from local to global to obtain a prediction matrix based on the multi-level filtering. The experimental results show that the MulFNet is better than the popular networks such as FCGF and PREDATOR on the standard 3DMatch. The registration accuracy of the MulFNet on the scanning dataset with an average overlap rate of 10% is 40.9% and 85.4% higher than the ICP and the GeoTransformer， respectively. It is verified that the proposed network can effectively solve the problem of low-overlap point cloud matching distortion.