湖南大学学报（自然科学版）2024，Vol.51Issue(8)：1-12,12.DOI:10.16339/j.cnki.hdxbzkb.2024273
基于语义辅助和深度时序一致性约束的自监督单目深度估计
Self-supervised Monocular Depth Estimation Based on Semantic Assistance and Depth Temporal Consistency Constraints
摘要
Abstract
Self-supervised monocular depth estimation methods trained on sequences of monocular images have received considerable attention in recent years by using the photometric consistency loss between adjacent frames instead of depth labels as the supervisory signal for network training.The photometric consistency constraint follows the static world assumption,but the moving objects in the monocular image sequence violate this assumption,which affects the camera pose estimation accuracy and the calculation accuracy of the photometric loss function during the self-supervised training process.By detecting and removing the moving target area,the camera pose decoupled from the target motion can be obtained,and the in fluence of the moving target area on the calculation accuracy of the photometric loss can be discarded.To this end,this paper proposes a self-supervised monocular depth estimation network based on semantic assistance and depth temporal consistency constraints.First,an offline instance segmentation network is used to detect dynamic category objects that may violate the static world assumption,and the corresponding region input pose network is removed to obtain a camera pose decoupled from object motion.Secondly,based on semantic consistency and photometric consistency constraints,the motion status of dynamic category targets is detected so that the photometric loss in the moving area does not affect the iterative update of network parameters.Finally,depth temporal consistency constraints are imposed in non-motion areas,and the estimated depth value of the current frame is explicitly aligned with the projected depth value of adjacent frames to further refine the depth prediction results.Experiments on the KITTI,DDAD and KITTI Odometry datasets verify that the proposed method has better performance than previous self-supervised monocular depth estimation methods.关键词
单目深度估计/自监督学习/运动目标/时序一致性Key words
monocular depth estimation/self supervision learning/moving object/temporal consistency分类
信息技术与安全科学引用本文复制引用
凌传武,陈华,徐大勇,张小刚..基于语义辅助和深度时序一致性约束的自监督单目深度估计[J].湖南大学学报（自然科学版）,2024,51(8):1-12,12.基金项目
国家自然科学基金资助项目(62171184,62273139,62106072),National Natural Science Foundation of China(62171184,62273139,62106072) （62171184,62273139,62106072）
国家自然科学基金区域联合重点项目(U23A20385),Joint Funds of the National Natural Science Foundation of China(U23A20385) （U23A20385）
国防预研项目(JCY2021206B015),National Defense Pre-research Foundation(JCY2021206B015) （JCY2021206B015）
