Deep Learning Advances on Different 3D Data Representations: A Survey

3D data is a valuable asset in the field of computer vision as it provides rich information about the full geometry of sensed objects and scenes. With the recent availability of large 3D datasets and the increase in computational power, it is today possible to consider applying deep learning to learn specific tasks on 3D data such as segmentation, recognition and correspondence. Depending on the considered 3D data representation, different challenges may be foreseen in using existent deep learning architectures. In this paper, we provide a comprehensive overview of various 3D data representations highlighting the difference between Euclidean and non-Euclidean ones. We also discuss how deep learning methods are applied on each representation, analyzing the challenges to overcome.

Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network

Facial landmark point localization is a typical problem in computer vision and is extensively used for increasing accuracy of face recognition, facial expression analysis, face animation etc. In recent years, substantial effort have been deployed by many researcher to design a robust facial landmark detection system. However, it still remains as one of the most challenging tasks due to the existence of extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this paper, we propose a novel coarse-to-fine deep recurrent-neural-network (RNN) based framework, which uses heat-map images for facial landmark point localization. The use of heat-map images allows us using the entire face image instead of the face initialization bounding boxes or patch images around the landmark points. Performance of our proposed framework shows significant improvement in case of handling difficult face images with higher degree of occlusion, variation of pose, large yaw angles and illumination. In comparison with the best current state-of-the-art technique a reduction of 45% in failure rate and an improvement of 11.5% in area under the curve for 300-W private test set are some of the main contributions of our proposed framework.