With the proposal of Industry 4.0 and the rapid development of machine vision in recent years, the realization of an intelligent robot with a vision system has become a reality. The visual system is mainly divided into two-dimensional (2D) vision and three-dimensional (3D) vision. 3D vision has the advantages of high precision, rich degree of freedom, and more application scenes, and is increasingly favored by the market. Compared with traditional 2D vision, 3D vision can obtain more comprehensive 3D data information, and is not affected by illumination. However, the resolution of X and Y directions is low, so the traditional hand-eye calibration algorithm based on checkerboard is not suitable for hand-eye calibration of 3D cameras and robots. Therefore, a hand-eye calibration method for robot and 3D camera based on 3D calibration block is designed. Through the camera calibration of gray information, the use of template matching algorithm segmentation calibration block of each plane, calculate the multiple points in plane in pixel coordinates of the location and the pixel coordinates of depth information at the same time, the fitting of the calibration block multiple plane normal vector information, through multiple plane intersection location feature point out. Random sample consensus algorithm was used to eliminate the wrong feature points, and according to the correct feature points, the transformation relationship between the point cloud data of the calibration block obtained by the robot under different poses was solved. The hand eye calibration results were solved based on the current coordinates of the robots, and the error evaluation model was established to analyze the results. Finally, the comparison between this method and the hand-eye calibration method using calibration plate was carried out by 6 degrees of freedom robot arm of the DENSON and Cognex EA-5000 camera. The results show that this method can complete the calibration more quickly and accurately.