Center point localization is a major factor affecting the performance of 3D single object tracking. Point clouds themselves are a set of discrete points on the local surface of an object, and there is also a lot of noise in the labeling. Therefore, directly regressing the center coordinates is not very reasonable. Existing methods usually use volumetric-based, point-based, and view-based methods, with a relatively single modality. In addition, the sampling strategies commonly used usually result in the loss of object information, and holistic and detailed information is beneficial for object localization. To address these challenges, we propose a novel Multi-view unsupervised center Uncertainty 3D single object Tracker (MUT). MUT models the potential uncertainty of center coordinates localization using an unsupervised manner, allowing the model to learn the true distribution. By projecting point clouds, MUT can obtain multi-view depth map features, realize efficient knowledge transfer from 2D to 3D, and provide another modality information for the tracker. We also propose a former attraction probability sampling strategy that preserves object information. By using both holistic and detailed descriptors of point clouds, the tracker can have a more comprehensive understanding of the tracking environment. Experimental results show that the proposed MUT network outperforms the baseline models on the KITTI dataset by 0.8% and 0.6% in precision and success rate, respectively, and on the NuScenes dataset by 1.4%, and 6.1% in precision and success rate, respectively. The code is made available at https://github.com/abchears/MUT.git.

Humongous amounts of data bring various challenges to face image retrieval. This paper proposes an efficient method to solve those problems. Firstly, we use accurate facial landmark locations as shape features. Secondly, we utilise shape priors to provide discriminative texture features for convolutional neural networks. These shape and texture features are fused to make the learned representation more robust. Finally, in order to increase efficiency, a coarse-to-fine search mechanism is exploited to efficiently find similar objects. Extensive experiments on the CASIA-WebFace, MSRA-CFW, and LFW datasets illustrate the superiority of our method.