Unsupervised convolutional neural network-based monocular scene depth estimation method

• Main Authors: ZHANG BO, LIN ZHENGKUI, YUE XIAOTONG, JIANG TONGBANG, MA QIAN, WANG NAIYAO, LIU HONGBO, YANG LIPING
• Format: Patent
• Language: Chinese; English
• Published: 26.11.2019
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS

The invention discloses an unsupervised convolutional neural network-based monocular scene depth estimation method. The method comprises the following steps of obtaining a depth value of each pixel point of a target image; obtaining a camera pose value when the pixel coordinates on the target image are transferred to the next frame of image; constructing a loss function; and performing depth estimation based on an unsupervised conditional random field residual convolutional neural network scene. The problem that manual data labeling is difficult is well solved through an unsupervised method, manpower is saved, and economic benefits are improved. According to the invention, a linear chain conditional random field thought is adopted to realize feature expression of an original image. An unsupervised conditional random field residual convolutional neural network scene depth estimation model is formed by combining an unsupervised residual convolutional neural network scene depth estimationmodel. The model provide