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

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.