Automatic 2Dto3D Video Conversion using 3D Densely Connected Convolutional Networks We present a 3D densely connected convolutional networks(DenseNet3D) for the task of automatic 2Dto3D video conversion. In contrast to previous automatic 2Dto3D conversion algorithms, which have separate stages and need ground truth depth as supervision, our method uses spatial transformer networks to combine the separate stages so that our model can be trained endtoend and directly use stereo image pairs as supervision. We further propose a 3D densely connected convolutional networks(DenseNet3D), which replace the original convolution layer with 3D convolution in densely connected convolutional networks to capture the spatiotemporal characteristics of videos. The experiment shows that the network has better results and faster speed than existing stateoftheart methods.

Convolutional Neural Network Based UpSampling for Depth Video Intra Coding Depth video contains depth and disparity information of a scene, which is critical for 3D video systems. In this paper, a convolutional neural network (CNN) based block upsampling method is proposed to improve the efficiency of depth video intra coding. For each largest coding tree in a depth map, it is downsampled before sent into encoder and recovered into the original size in an intelligent way after lowresolution coding. A novel textureassisted CNN (TACNN) is presented to handle the depth block upsampling. The network is made up of several residual coding units and the features of texture block are extracted to assist the reconstruction of the corresponding depth block. Experimental results show that the proposed method achieves competitive ratedistortion performance compared with the stateoftheart approaches.