LpDepth:基于拉普拉斯金字塔的自监督单目深度估计  

LpDepth:Self-supervised Monocular Depth Estimation Based on Laplace Pyramid

作　　者：曹明伟 邢景杰 程宜风 赵海锋 CAO Mingwei;XING Jingjie;CHENG Yifeng;ZHAO Haifeng(School of Computer Science and Technology,Anhui University,Hefei 230601,China;State Grid Anhui Electric Power Research Institute,Hefei 230601,China)

机构地区：[1]安徽大学计算机科学与技术学院,合肥230601 [2]国网安徽省电力有限公司电力科学研究院,合肥230601

出　　处：《计算机科学》2025年第3期33-40,共8页Computer Science

基　　金：安徽省高校科研项目(2024AH050045);国家自然科学基金(62372153,62076005)。

摘　　要：自监督单目深度估计受到了国内外研究人员的广泛关注。现有基于深度学习的自监督单目深度估计方法主要采用编码器-解码器结构。然而,这些方法在编码过程中对输入图像进行下采样操作,导致部分图像信息,尤其是图像的边界信息丢失,进而影响深度图的精度。针对上述问题,提出一种基于拉普拉斯金字塔的自监督单目深度估计方法(Self-supervised Monocular Depth Estimation Based on the Laplace Pyramid,LpDepth)。此方法的核心思想是:首先,使用拉普拉斯残差图丰富编码特征,以弥补在下采样过程中丢失的特征信息;其次,在下采样过程中使用最大池化层突显和放大特征信息,使编码器在特征提取过程中更容易地提取到训练模型所需要的特征信息;最后,使用残差模块解决过拟合问题,提高解码器对特征的利用效率。在KITTI和Make3D等数据集上对所提方法进行了测试,同时将其与现有经典方法进行了比较。实验结果证明了所提方法的有效性。Self-supervised monocular depth estimation has attracted widespread attention from researchers both domestically and abroad.Existing self-supervised monocular depth estimation methods based on deep learning mainly use encoder-decoder structures.However,these methods perform down-sampling operations on the input image during the encoding process,resulting in the loss of some image information,particularly boundary information,which leads to the degradation of the accuracy of the estimated depth map.To address this issue,this paper proposes a new self-supervised monocular depth estimation method based on the Laplacian pyramid.Specifically,the method enriches the encoded features using Laplacian residual images,compensates for the loss of information during down-sampling,highlights and amplifies features during the down-sampling process using maximum-pooling layers,which facilitates feature extraction for model training by the encoder.The method also leverages residual modules to mitigate potential overfitting issues and improve the decoder’s efficiency in feature utilization.Finally,we test the proposed method on benchmark datasets such as KITTI and Make3D and compare its performance with state-of-the-art methods,with experimental results demonstrating the effectiveness of the proposed method.

关 键 词：单目深度估计 拉普拉斯金字塔 残差网络 深度图 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]