{"type":"ScholarlyArticle","title":"Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention","authors":[{"name":"Zizhang Wu","slug":"zizhang-wu","given":"Zizhang","family":"Wu","affiliation":"Fudan University"},{"name":"Zhuozheng Li","slug":"zhuozheng-li","given":"Zhuozheng","family":"Li","affiliation":"Fudan University"},{"name":"Zhi-Gang Fan","slug":"zhi-gang-fan","given":"Zhi-Gang","family":"Fan","affiliation":"Fudan University"},{"name":"Yunzhe Wu","slug":"yunzhe-wu","given":"Yunzhe","family":"Wu","affiliation":"Fudan University"},{"name":"Yuanzhu Gan","slug":"yuanzhu-gan","given":"Yuanzhu","family":"Gan","affiliation":"Fudan University"},{"name":"Jian Pu","slug":"jian-pu","given":"Jian","family":"Pu","affiliation":"Fudan University"}],"abstract":"The monocular depth estimation task has recently revealed encouraging prospects, especially for the autonomous driving task. To tackle the ill-posed problem of 3D geometric reasoning from 2D monocular images, multi-frame monocular methods are developed to leverage the perspective correlation information from sequential temporal frames. However, moving objects such as cars and trains usually violate the static scene assumption, leading to feature inconsistency deviation and misaligned cost values, which would mislead the optimization algorithm. In this work, we present CTA-Depth, a Context-aware Temporal Attention guided network for multi-frame monocular Depth estimation. Specifically, we first apply a multi-level attention enhancement module to integrate multi-level image features to obtain an initial depth and pose estimation. Then the proposed CTA-Refiner is adopted to alternatively optimize the depth and pose. During the CTA-Refiner process, context-aware temporal attention (CTA) is developed to capture the global temporal-context correlations to maintain the feature consistency and estimation integrity of moving objects. In particular, we propose a long-range geometry embedding (LGE) module to produce a long-range temporal geometry prior. Our approach achieves significant improvements (e.g., 13.5% for the Abs Rel metric on the KITTI dataset) over state-of-the-art approaches on three benchmark datasets.","doi":"10.24963/ijcai.2023/172","url":"https://ijcai.org/proceedings/2023/172","pdf":"https://ijcai.org/proceedings/2023/0172.pdf","markdown":"https://ijcai.org/proceedings/2023/172.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/172","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":172,"pages":{"first":1551,"last":1559},"keywords":["Computer Vision: CV: 3D computer vision","Machine Learning: ML: Attention models","Computer Vision: CV: Scene analysis and understanding"],"videos":[],"references":[{"position":1,"raw":"[Bae et al., 2022] Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla. 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