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.

Concerning the error replica recovery problem of cloud storage system with auditable multiple replicas， based on the multi-replica cloud storage integrity audit scheme， the error replica recovery mechanism was expounded from five aspects： overall process， influencing factors， recovery strategy， fault location and computation model； the error replica recovery strategies were summarized into four types： full-replica download and upload， full-replica difference upload， fault-block upload and fault-segment upload； the factors affecting the recovery efficiency were quantified； and the computation model for communication overhead， computation overhead and total overhead were proposed. For a specific multi-replica cloud storage integrity audit scheme， the overhead of correcting random errors of one data block under different strategies and parameters was analyzed quantitatively. Experimental results show that when the bandwidth is 1 Mb/s， 10 Mb/s， 100 Mb/s and 1 Gb/s respectively， the time cost of the optimal strategy in the experiment is only 0.34%， 2.44%， 15.27% and 46.93% respectively of that of the full-replica difference upload strategy. It can be seen that the proposed models can be used to select appropriate strategies and parameters for auditable multi-replica cloud storage system to improve the efficiency of recovering error replicas， especially in the case of limited network bandwidth.