DGOA:point cloud upsampling based on dynamic graph and offset attention
The point clouds obtained directly from 3D scanning equipment are often sparse,uneven,and noisy.Therefore,point cloud upsampling has become increasingly vital in fields such as point cloud reconstruction and rendering.A new point cloud upsampling network named DGOA was proposed based on Dynamic Graph and Offset Attention.DGOA mainly consisted of three modules:LFE(local feature extraction),GFE(global feature extraction),and CR(coordinate reconstruction).LFE utilized a multi-layer structure to extract neighborhood information,constructed a dynamic graph based on feature similarity at each layer,and adaptively grouped point clouds in the feature space.This increased the receptive field,obtained long-distance semantic information,and more effectively modeled the local geometry of the point cloud.GFE employed offset attention based on the Laplace operator,enabling each point to obtain global information of the point cloud.This ensured that the details of the generated point cloud were consistent with the original point cloud and reduced the impact of noise.CR,inspired by the FoldingNet operation,prevented the generated points from clustering together.In addition,the entire network was permutation invariant with respect to the order of points in the input point cloud.Quantitative and qualitative experimental results on multiple datasets demonstrated that the proposed method outperformed other methods and exhibited good generalization and stability.
point cloudpoint cloud upsamplingdynamic graphoffset attentiondeep learning