步态识别在身份识别领域具有重要的研究意义。随着技术的发展,步态识别的研究热点正从二维（2D）转向三维（3D）。与图像固有的2D信息相比,用视觉技术还原的3D信息能更有效地预测人员的身份。在2D视觉领域中,由于受到物体遮挡、视角变化等因素的影响,传统的步态识别方法在实际应用中难以取得理想的识别性能。基于人体3D重建和人体3D姿态估计等3D人体技术,近年来的研究在3D步态识别领域取得了一系列进展。本文介绍了3D步态识别方法,探讨了基于3D步态的身份识别领域的研究现状、优势与不足；总结了主要的3D步态数据集；讨论了3D识别方法与2D识别方法的对比；提出了3D身份识别领域未来潜在的研究方向,包括3D数据集的采集和整理、2D 和 3D 数据的多模态融合等。

Gait recognition is a new biometric identification technology that uses human walking posture and gait to determine a person’s identity. Face recognition, which is considered traditional biometric recognition technology, is widely used, but it has the following defects： 1） recognition distance is limited； 2） it is vulnerable to occlusion and light and other factors； and 3） the results are at risk of being attacked by using face photos, video playback, and three-dimensional masks. In contrast, gait has the following advantages： 1） it can be identified from a long distance； 2） it is less affected by occlusion and illumination； and 3） it is not easy to disguise and deceive. Therefore, gait recognition is playing an increasingly important role in public safety, biometric authentication, video surveillance, and other fields. Gait recognition is mainly divided into two categories according to the dimension of input data： 1） two-dimensional （2D） gait recognition based on 2D data and 2） three-dimensional （3D） gait recognition based on 3D data. At present, the research review in the field of gait recognition focuses on 2D gait recognition, usually from the perspective of traditional machine learning or deep learning. Gait recognition is moving from 2D to 3D. Compared with the inherent 2D information of the image, the 3D information restored by visual technology can more effectively predict human identity. In the field of 2D vision, traditional gait recognition methods have difficulty achieving better recognition performance because of the influence of object occlusion and view changes. On the basis of 3D human technology such as 3D human reconstruction and 3D human pose estimation, a series of progress has been made in the field of 3D gait recognition in recent years. To fully understand the existing research in the field of 3D gait recognition, this paper reviews and summarizes the research in this field. This paper discusses the research status, advantages, and disadvantages of identity recognition based on 3D gait； summarizes 3D gait recognition methods and 3D gait datasets； and provides potential research directions in the field of 3D identity recognition. This paper summarizes the different input data of existing 3D gait recognition methods and the recognition effect （recognition accuracy and speed） of these methods. These methods include multi-view-based, depth image-based, 3D skeleton-based, 3D point cloud-based, and 3D reconstruction-based recognition methods. This paper divides the 3D gait dataset into the indoor dataset and the outdoor dataset according to the acquisition environment. The 3D gait data include depth images, 3D skeleton, 3D human body grid, and 3D point cloud. In addition, this paper collates and compares the experimental results of different 3D gait recognition methods on various 3D gait datasets. Finally, this paper provides potential research directions for the field of 3D identity recognition. 1） Performance improvement and model optimization. Different from 2D gait, the performance of 3D gait is closely related to the 3D model. The 3D deep learning model needs to be optimized to improve the recognition performance of 3D gait in real scenes. For example, the training skills of vision Transformer （ViT） to improve performance are applied to 3D models such as 3D convolutional neural networks to improve the generalization and robustness of the model. The 3D model with the ViT concept is expected to learn more discriminative features from 3D gait data. 2） Collection and collation of 3D datasets. Compared with the 2D gait data set, the number of 3D gait datasets is small and the data types are not rich enough, which limits the development of 3D gait and requires further data collection by researchers. When the collected 3D gait dataset is sorted out, the training set and the test set can be divided in advance. For the test set, the registration set and the verification set are expected to be divided again, and the baseline algorithm that is easy to reproduce is used for evaluation. Rank-1 accuracy and mean average precision can be used as evaluation metrics. 3） Multi-modal fusion of 2D and 3D data. Compared with 2D data, 3D data contains more information, so the effective use of 3D data can improve the recognition performance in real scenes. In the field of gait recognition, current research mainly focuses on 2D data （human 2D skeleton, gait contour map, etc.） but has gradually shifted to 3D data （human 3D skeleton, human 3D mesh, depth image, etc.） in recent years. Future researchers can explore multidimensional gait recognition networks based on multimodal fusion to dynamically fuse 2D and 3D gait data. This fusion network combines the advantages of high 2D recognition efficiency and high 3D recognition accuracy and is expected to improve the performance of gait recognition in complex outdoor scenes. 4） Promotion and application of 3D vision technology. This paper mainly discusses the application of 3D vision technology in the emerging field of biometrics, particularly in gait recognition. Traditional biometrics, such as face recognition and fingerprint recognition, are also gradually transitioning from 2D to 3D. It is anticipated that this paper will aid researchers in understanding the latest advancements in 3D gait recognition and inspire the development of novel and advanced algorithms and models.