6 Degree of Freedom （DoF） pose estimation is a key technology in computer vision and robotics， and has become a crucial task in the fields such as robot operation， automatic driving， augmented reality by estimating 6 DoF pose of an object from a given input image， that is， 3 DoF translation and 3 DoF rotation. Firstly， the concept of 6 DoF pose and the problems of traditional methods based on feature point correspondence， template matching， and three-dimensional feature descriptors were introduced. Then， the current mainstream 6 DoF pose estimation algorithms based on deep learning were introduced in detail from different angles of feature correspondence-based， pixel voting-based， regression-based and multi-object instances-oriented， synthesis data-oriented， and category level-oriented. At the same time， the datasets and evaluation indicators commonly used in pose estimation were summarized and sorted out， and some algorithms were evaluated experimentally to show their performance. Finally， the challenges and the key research directions in the future of pose estimation were given.