Coding-Net: A multi-purpose neural network for Fringe Projection Profilometry

Fringe Projection Profilometry is one of the important techniques in three-dimensional vision. However, traditional Fringe Projection Profilometry employing a lot of patterns has significant challenges for efficiently recovering the absolute phase. In this paper, a multi-purpose neural network combining with code-based patterns is proposed to recover the absolute phase firstly, which can greatly decrease the number of patterns with high accuracy. Different from the traditional approach of trigonometric calculation, the well-trained multi-purpose network can learn the principle of extracting absolute phase from few patterns. Experiments demonstrate that the proposed method can acquire high accuracy for complex texture objects, indicating potential application in precision engineering with high speed.

Automated summary

A method combining neural network and code-based patterns is proposed in this paper for efficiently recovering the absolute phase with high accuracy. Through a small number of patterns, high-precision measurement of complex texture objects can be achieved.