The invention discloses a
Gaussian splash
monocular reconstruction method based on a neural network, and the method comprises the steps: constructing a
Gaussian splash
monocular reconstruction model which comprises an image depth
recovery model and an SfM
motion recovery model, training the image depth
recovery model, shooting a panoramic picture, training the panoramic picture through spherical
convolution, recovering a
distortion part, and carrying out the reconstruction of the
Gaussian splash
monocular reconstruction model; then sparse
point cloud reconstruction is carried out through an SfM
motion recovery model, a Gaussian reconstruction core is used for reconstructing the surface of the image to form a basic
geometric configuration of the image, and finally, an
image processing technology is used for carrying out Gaussian low-pass filtering
noise reduction
processing on the image to form a complete
surface structure of an object, and three-dimensional reconstruction of an image scene is completed. According to the method, the three-dimensional Gaussian splashing technology is combined with the spherical
convolution kernel to construct the geometric model of the object, the
operability is remarkably improved, the calculation speed is greatly increased, and the method has important application value in the fields of intelligent
medical treatment,
engineering flaw detection and the like.