一种基于U型全卷积神经网络的深度估计模型

Image Depth Estimation Model Based on Fully Convolutional U-Net

本文解决了从单张图像估计深度信息的问题。单张图像与深度图之间的映射是是模棱两可的,它需要全局信息和局部信息。本文部署了一个全卷积U型神经网络,它用预训练的ResNet-50网络提取图像特征,然后用残差上采样模块将特征图恢复到深度图的尺寸大小,并且使用了跳跃链接,整个网络呈现U型,从而对全局信息和局部信息进行融合。整个网络可以进行端到端的训练。

The problem of depth estimation from single image has been addressed. The mapping between a single image and the depth map is inherently ambiguous, and requires both global and local information. This paper presents a fully convolutional U-net whose encoder is pretrained ResNet50 without fully connected layer or pooling layer, and uses residual up-sampling layers to enlarge the feature maps. Besides, skip connection is introduced, making the model U-net, to fuse global and local information. The network can be end-to-end trained.