机器人2017，Vol.39Issue(6)：812-819,8.DOI:10.13973/j.cnki.robot.2017.0812
基于结构化深度学习的单目图像深度估计
Structured Deep Learning Based Depth Estimation from a Monocular Image
摘要
Abstract
For the purposes of extracting rich 3D structural features from a monocular image and inferring depth informa-tion for the scene, a structured deep learning model is proposed for the task of depth estimation from a monocular image. The model combines a novel multi-scale convolutional neural network (CNN) and continuous conditional random field (CCRF) in a unified deep learning framework. CNN can learn related feature representations from an image, and CCRF can optimize the output of CNN according to the position and color information of the image pixels. By jointly learning the parameters of CCRF and CNN, the generalization ability of the model can be improved. Experiments on NYU Depth dataset demonstrate the effectiveness and superiority of the model. The average relative error of the predictions of the model is 0.187, and the root mean squared error is 0.074, the average log10 error is 0.671.关键词
深度估计/卷积神经网络/单目图像/结构化深度学习/条件随机场Key words
depth estimation/convolutional neural network/monocular image/structured deep learning/conditional ran-dom field分类
信息技术与安全科学引用本文复制引用
李耀宇,王宏民,张一帆,卢汉清..基于结构化深度学习的单目图像深度估计[J].机器人,2017,39(6):812-819,8.基金项目
国家自然科学基金(61572500). （61572500）
