A Novel Approach to Droplet's 3D Shape Recovery Based on Mask R-CNN and Improved Lambert⁻Phong Model.Aiming at the demand for extracting the three-dimensional shapes of droplets in microelectronic packaging, life science, and some related fields, as well as the problems of complex calculation and slow running speed of conventional shape from shading (SFS) illumination reflection models, this paper proposes a Lambert⁻Phong hybrid model algorithm to recover the 3D shapes of micro-droplets based on the mask regions with convolutional neural network features (R-CNN) method to extract the highlight region of the droplet surface.  First, the Mask R-CNN network is used to realize the segmentation of the highlight region of the droplet and obtain its coordinate information.  The experimental results show that the proposed algorithm based on mask R-CNN had better precision and shorter running time. 
Mask R-CNN
A Novel Approach to Droplet's 3D Shape Recovery Based on Mask R-CNN and Improved Lambert⁻Phong Model.Aiming at the demand for extracting the three-dimensional shapes of droplets in microelectronic packaging, life science, and some related fields, as well as the problems of complex calculation and slow running speed of conventional shape from shading (SFS) illumination reflection models, this paper proposes a Lambert⁻Phong hybrid model algorithm to recover the 3D shapes of micro-droplets based on the mask regions with convolutional neural network features (R-CNN) method to extract the highlight region of the droplet surface. 
Lambert⁻Phong Model