Multi-level filter network for low-overlap point cloud registration
Aiming at the problem of matching distortion caused by structural occlusion,field of view con-straints,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 in-formation 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 ob-tain 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 registra-tion 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 net-work can effectively solve the problem of low-overlap point cloud matching distortion.
point cloud registrationmatching distortionlow-overlap point cloudmulti-level filterpar-tial measurement
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