Depth Estimation Method Based on Image Reconstruction
Achieving depth estimation with reliable accuracy is the key to 3D target detection methods,and an image depth estimation method is proposed.Based on the deep learning method,depth estimation is achieved by training a deep neural network to reconstruct another image from one image of a stereoscopic image,and minimizing depth error is used instead of minimizing parallax error in training,and the geometric constraint of stereoscopic image pair is used to introduce the left and right view consistency loss to achieve more accurate depth estimation.Aiming at the problem of difficulty in obtaining image real depth data and high cost of dataset production,an image depth estimation framework based on image reconstruction self-supervised training is constructed,which does not require image real depth data and saves the cost of dataset production.Aiming at the problem that the depth estimation error increases sharply with the increase of depth,the minimizing depth error is used instead of minimizing parallax error,which solves the problem that the depth estimation network overemphasizes the small depth error in the near and ignores the depth error in the distance.In addition,we also make full use of the geometric constraints of stereoscopic image pairs,and introduce the loss of left-right view consistency in training to improve the accuracy of depth estimation.Experiments verify that the proposed image depth estimation method outperforms other existing methods,and has better performance when performing depth estimation in distant areas and small targets.
3D object detectiondepth estimationimage reconstructionself-supervised learningdeep neural networks
国家科技期刊平台
NSTL
万方数据
