现代信息科技2024，Vol.8Issue(5)：106-110,5.DOI:10.19850/j.cnki.2096-4706.2024.05.023
基于注意力机制和多级校正的单目室内场景深度估计
Depth Estimation of Monocular Indoor Scenes Based on Attention Mechanism and Multi-level Correction
摘要
Abstract
The depth estimation of scenes has a wide range of applications in the field of 3D vision.A monocular depth estimation network based on Attention Mechanism and multi-level correction is proposed to address the issues of low accuracy and poor prediction ability of fine-grained information in monocular indoor scene depth estimation.The network first uses a dual branch module with a self attention Transformer and a convolutional neural network to extract multi-resolution features of color images.Then,a module based on spatial domain Attention Mechanism is used to gradually fuse the extracted multi-resolution features.Finally,the fused features are processed through multi-level correction,and depth images with different resolutions are gradually estimated.The experimental results show that compared with similar methods,the proposed network can effectively improve the predictive ability of fine-grained information in depth images,and multiple evaluation indicators of the network have been improved to varying degrees.关键词
单目深度估计/Transformer/注意力机制/多级校正Key words
monocular depth estimation/Transformer/Attention Mechanism/multi-level correction分类
信息技术与安全科学引用本文复制引用
刘鹏,丁爱华,窦新宇..基于注意力机制和多级校正的单目室内场景深度估计[J].现代信息科技,2024,8(5):106-110,5.基金项目
唐山市市级科技计划项目(22130205H) （22130205H）
