王一良
江南大学轻工过程先进控制教育部重点实验室 无锡 214122
基金项目:国家自然科学基金(61573168)
详细信息
作者简介:陈莹：女，1976年生，教授，博士，研究方向为信息融合、模式识别
王一良：男，1997年生，硕士生，研究方向为计算机视觉与模式识别
通讯作者:陈莹 chenying@jiangnan.edu.cn
中图分类号:TN911.73; TP391计量
文章访问数:448
HTML全文浏览量:179
PDF下载量:65
被引次数:0
出版历程
收稿日期:2020-07-17
修回日期:2020-12-29
网络出版日期:2021-02-03
刊出日期:2021-10-18
Unsupervised Monocular Depth Estimation Based on Dense Feature Fusion
Ying CHEN,,Yiliang WANG
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
Funds:The National Natural Science Foundation of China (61573168)
摘要
摘要:针对无监督单目深度估计生成深度图质量低、边界模糊、伪影过多等问题，该文提出基于密集特征融合的深度网络编解码结构。设计密集特征融合层(DFFL)并将其以密集连接的形式填充U型编解码器，同时精简编码器部分，实现编、解码器的性能均衡。在训练过程中，将校正后的双目图像输入给网络，以重构视图的相似性约束网络生成视差图。测试时，根据已知的相机基线距离与焦距将生成的视差图转换为深度图。在KITTI数据集上的实验结果表明，该方法在预测精度和误差值上优于现有的算法。
关键词:深度估计/
无监督/
密集特征融合层/
编解码器
Abstract:In view of the problems of low quality, blurred borders and excessive artifacts generated by unsupervised monocular depth estimation, a deep network encoder-decoder structure based on dense feature fusion is proposed. A Dense Feature Fusion Layer(DFFL) is designed and it is filled with U-shaped encoder-decoder in the form of dense connection, while simplifying the encoder part to achieve a balanced performance of the encoder and decoder. During the training process, the calibrated stereo pair is input to the network to constrain the network to generate disparity maps by the similarity of reconstructed views. During the test process, the generated disparity map is converted into a depth map based on the known camera baseline distance and focal length. The experimental results on the KITTI data set show that this method is superior to the existing algorithms in terms of prediction accuracy and error value.
Key words:Depth estimation/
Unsupervised learning/
Dense Feature Fusion Layer(DFFL)/
Encoder-decoder
PDF全文下载地址:
https://jeit.ac.cn/article/exportPdf?id=89537a7d-6e55-447f-a556-c4c70a86b0cc
