Feature Enhanced Fully Convolutional Networks for Monocular Depth Estimation In the research of monocular image depth estimation, the fully convolutional residual network structure can achieve better prediction results, but the precision of image depth prediction still has room for improvement. Therefore, this paper proposes a feature of enhanced fully convolutional networks (FEFCN). Firstly, a feature pyramid structure is introduced to fuse multiscale feature maps extracted from downsampling with the result of upsampling. By reusing feature maps, the image depth prediction maps become richer and more delicate. Secondly, the highfrequency information of the input image is extracted by a highpass filter and fused with upsampling results in fully convolutional networks, which further reduces the error and improves the accuracy. In addition, by preprocessing the raw data in NYU dataset, a new dataset is generated for training and testing. Finally, the experimental results prove that the proposed method improves the quality of depth estimation maps. At the same time, the rms and rms(log) of image depth prediction error were reduced by 13.96% and 10.25% respectively.

Depth Map Inpainting Using a Fully Convolutional Network Emerging consumer depth cameras, such as Microsoft Kinect and ASUS Xtion Pro, are lowcost depth sensors which can capture the depth maps of the scene, and obtaining color images of the corresponding scenes at the same time. Depth information is very important for computer vision applications, and to acquire the accurate depth information is challengeable. However, due to the limitations of imaging principle, depth maps acquired by such devices are often noisy and will lose depth values for certain areas. In this paper, we present a novel method for depth inpainting problem using a Fully Convolutional Network (FCN). The network has two input branches. One branch is a color image that has been processed by a convoluted hidden layer, and the other one is the missing depth map. Besides, we add a shortcut in order to improve the feature extraction capability of the network. We have validated our method in the Middlebury Stereo Dataset and realworld scenes captured by Kinect 2.0. Experiments show that our method has better applicability and reliability than traditional methods in depth inpainting.