Point cloud data enhancement based on homomorphic filter

Point cloud data of complex product with small changes of surface has narrow data range, high spatial relativity and smooth data change. It is not sensitive to feature extraction and shape recognition. Homomorphic filter is introduced into point cloud data enhancement. Point cloud data reflecting the product shape and spacial relationship is divided into the low frequency part and the high frequency part. Spatial location change has little effect on the low frequency part. The high frequency part changes greatly with spatial location characteristics of the object itself. Homomorphic filter is designed to reduce the low frequency part and enhance high-frequency part. Point cloud data enhancement based on homomorphic filter is proposed. Example shows point cloud data enhancement based on homomorphic filter can convert non uniform distribution point cloud data to uniform distribution point cloud data, improving the contrast of point cloud data.