摘要
为解决无监督单目深度估计边缘深度估计不准确的问题,提出了一种基于边缘强化的无监督单目深度估计网络模型。该模型由单视图深度网络和姿态网络两部分构成,均采用编解码结构,其中单视图深度网络编码器使用高分辨率网络(high-resolution net,HRNet)作为骨干网络,在整个过程中保持高分辨率表示,有利于提取精确空间特征;单视图深度网络解码器引入条状卷积,细化深度边缘附近的深度变化,同时利用经典的高斯拉普拉斯算子增强边缘细节,最终充分利用深度边缘信息提高深度估计质量。在KITTI数据集中进行的实验结果表明:所提模型具有较好的深度估计性能,能够使深度图中的目标边缘更加清晰,细节更加丰富。
To solve the problem of poor edge depth estimation accuracy in unsupervised monocular depth estimation,an unsupervised monocular depth estimation model based on edge enhancement is proposed.The model is composed of a single-view depth network and a camera pose estimation network,both of which adopt encoder-decoder structures.The single-view depth network encoder uses high-resolution net(HRNet)as the backbone which maintains high resolution representations throughout the whole process,and is conducive to extract accurate spatial features;The single-view depth network decoder introduces strip convolutions to refine the depth variations near the edges,while enhancing the edge details using the classical Laplace of Gaussian operator.The method fully utilizes the depth edge information to improve the quality of the depth estimation.The experimental results on the KITTI dataset show that the proposed model has good depth estimation performance,making the edges of the depth map clearer with more details.
作者
曲熠
陈莹
QU Yi;CHEN Ying(Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education),Jiangnan University,Wuxi 214122,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2024年第1期71-79,共9页
Systems Engineering and Electronics
基金
国家自然科学基金(62173160)资助课题。
关键词
单目深度估计
无监督学习
条状卷积
边缘增强
monocular depth estimation
unsupervised learning
strip convolutions
edge enhancement
