**6. Conclusions**

In this paper, we propose a novel approach to estimate landmarks on 3D geometry data. By transforming the 3D data to 2D attribute maps, the goal of our approach is to predict the landmarks based on the attribute maps. Different from using the handcrafted feature, we feed the global and the local attribute maps into the deep CNN model to extract global and local feature. Based on coarse-to-fine strategy, a global model is trained to estimate landmarks roughly and local models are trained to refine the landmarks' location. Evaluated on the Bosphorus dataset, the proposed method performs more effectively than handcrafted features and other pre-trained models. Compared with other existing methods, the results on the Bosphorus dataset and BU-3DFE dataset have also demonstrated comparable performance, especially in some common landmarks.

In the future, some other issues of improving the robustness under other challenging conditions such as self-occlusion and data missing will be studied. In addition, using decision fusion of simple classifiers to balance the computation complexity and the accuracy may be another effective method for this problem.

**Author Contributions:** K.W. designed the algorithm, conceived of, designed and performed the experiments, analyzed the data and wrote this paper. X.Z. provided the most important comments and suggestions, and also revised the paper. W.G. and J.Z. provided some suggestions and comments for the performance improvement of the algorithm.

**Funding:** This research received no external funding.

**Acknowledgments:** This work was supported by the Natural Science Foundation of China (Grant No. 91746111, Grant No. 71702143), the Ministry of Education and China Mobile Joint Research Fund Program (No. MCM20160302), the Shaanxi Provincial Development and Reform Commission (No. SFG2016789), and the Xi'an Science and Technology Bureau (No. 2017111SF/RK005-(7)).

**Conflicts of Interest:** The authors declare no conflict of interest.
