Unsupervised Monocular Depth Estimation Based on Scale Clue Enhancement
Due to the relationship of one-to-many between images and depth maps in monocular depth estimation,there is a problem of scale ambiguity in monocular depth estimation itself.In order to improve the inherent ambiguity prob-lem in geometric modeling of monocular depth estimation,this paper introduces a monocular multi-frame depth estimation method based on multi-view stereo(MVS)to construct moving depth and dig the scale clues.The traditional monocular depth estimation and MVS depth estimation are organically combined to improve the inherent ambiguity problem in the geo-metric modeling of monocular depth estimation.On this basis,two channel attention modules are designed to improve the network's ability to perceive scene structures and process local information,so as to more fully integrate features of differ-ent scales and produce more accurate and clearer depth maps.In the test results of the KITTI dataset,the average relative er-ror and square relative error of this paper have been improved by 4.7%and 8.0%respectively compared to the baseline net-work,with all error and accuracy indicators surpassing other mainstream unsupervised monocular depth estimation methods.
万方数据
维普
