3D material surface reconstruction and repair based on Poisson algorithm and multi-scale feature coding network
The blast furnace smelting is carried out in a completely closed and high-pressure environ-ment,it is impossible to observe the internal operating conditions of the blast furnace and the shape of the material surface,making it difficult to accurately judge the furnace condition,and the utilization rate of the material surface data resources is not high,which affects the operator's adjustment of the distribution system of the furnace top.In order to improve the data utilization rate and improve the quality and accuracy of point cloud data,a double-sided filter was proposed to preprocess the three-dimensional point cloud data of the original blast material surface in the paper.Poisson reconstruction algorithm is used to reconstruct the filtered point cloud data,build a multi-scale feature coding net-work,and repair the missing 3D point cloud material surface.Poisson surface reconstruction can re-tain the detail characteristics of the surface and smooth the surface,which provides an important basis for quickly judging the type of the surface.By extracting the point cloud feature information of differ-ent scales,3D point cloud feature enhancement and multi-level expression are realized.Experiments show that the proposed method has small error in point cloud missing prediction and complete shape of point cloud,which provides a fast,efficient and practical solution for processing point cloud data with missing material surface.
blast furnace material surfacepoint cloud denoisingPoisson surface reconstructionneu-ral networkmulti-scale
NETL
NSTL
万方数据
