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Article
Geochemistry & Geophysics
Vlad-Cristian Miclea et al.
Summary: Environment perception by computing depth is crucial for unmanned aerial vehicle (UAV) systems. However, due to limited payload capacity, most drones are equipped with a single camera, making it challenging to use depth perception methods based on LiDAR or stereo reconstruction. In this study, we propose a novel approach for monocular depth estimation (MDE) on aerial images using a customized CNN. Additionally, we introduce a learning-based correction method to improve accuracy at both short and long ranges. Our method demonstrates accurate and reliable depth estimation in various aerial scenarios, as shown on synthetic and real-life images captured by a drone.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Rene Ranftl et al.
Summary: The success of monocular depth estimation relies on large and diverse training sets. This study proposes tools and methods to mix different datasets and improve the performance of monocular depth estimation.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Minwoo Kim et al.
Summary: This paper proposes an obstacle avoidance strategy for small multi-rotor drones using deep reinforcement learning. The proposed method consists of depth estimation and navigation decision making steps. Simulations and real experiments using a Parrot Bebop2 drone demonstrate the performance and robustness of the proposed approach.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Chemistry, Analytical
Armin Masoumian et al.
Summary: This paper provides a state-of-the-art review of the current developments in monocular depth estimation (MDE) based on deep learning techniques. It highlights the key points from various aspects and discusses limitations and future research directions in the field.
Article
Computer Science, Artificial Intelligence
Hao Xing et al.
Summary: In this paper, a novel approach is proposed to combine structure information from a promising Plane and Parallax geometry pipeline with depth information into a U-Net supervised learning network. The model achieves better performance compared to existing methods, especially in the depth prediction of thin objects and edges.
PATTERN RECOGNITION
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Philipp Schroeppel et al.
Summary: Recent deep learning approaches in multi-view depth estimation are utilized in depth-from-video or multi-view stereo settings. These approaches are technically similar as they correlate multiple source views with a keyview to estimate a depth map for the keyview. This work introduces the Robust Multi-view Depth Benchmark that evaluates performance using public datasets in different domains. It is found that recent approaches do not generalize well across datasets when camera poses are available, due to their cost volume output running out of distribution. To address this, the Robust MVD Baseline model is proposed, which employs a novel scale augmentation procedure for robust multi-view depth estimation.
2022 INTERNATIONAL CONFERENCE ON 3D VISION, 3DV
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Horatiu Florea et al.
Summary: This paper conducts a survey on existing research specifically tailored for monocular depth estimation from aerial views. It also introduces the datasets and tools supporting such research and highlights the challenges in this field.
2022 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING, ICCP
(2022)
Review
Computer Science, Artificial Intelligence
Yue Ming et al.
Summary: This paper reviews the state-of-the-art development in deep learning-based monocular depth estimation, covering deep learning models, method categorization, datasets, evaluation metrics, and challenges for future research directions.
Article
Robotics
Jaewan Choi et al.
Summary: This study proposes a framework for decentralized collision avoidance using deep reinforcement learning, where each agent can independently make decisions; the mobile robot agents learn how to avoid obstacles and reach target points in environments with irregularly moving dynamic obstacles to test the effectiveness of the proposed method.
INTELLIGENT SERVICE ROBOTICS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Jamie Watson et al.
Summary: The study introduces a new self-supervised monocular depth estimation method called ManyDepth, which can utilize sequence information at test time, and experiments show that it outperforms existing self-supervised baselines.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Arda Duzceker et al.
Summary: The proposed approach improves depth prediction performance by leveraging previous scene geometry information and utilizing ConvLSTM cells to propagate hidden states through viewpoint changes, while maintaining an advantage in real-time performance.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Jing Liu et al.
Summary: In this paper, an end-to-end multi-scale residual pyramid attention network (MRPAN) is proposed to solve the challenging problem of monocular depth estimation in computer vision. By introducing a multi-scale attention context aggregation module and an improved residual refinement module, the network is able to better recover complex structures and retain more local details in scenes.
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
(2021)
Article
Computer Science, Software Engineering
Xuan Luo et al.
ACM TRANSACTIONS ON GRAPHICS
(2020)
Review
Engineering, Multidisciplinary
ChaoQiang Zhao et al.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Chenxu Luo et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2020)
Article
Robotics
Vaishakh Patil et al.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2020)
Article
Engineering, Electrical & Electronic
Liqin Huang et al.
IEEE SIGNAL PROCESSING LETTERS
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Arun C. S. Kumar et al.
PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW)
(2018)
Article
Mathematics
Onur Özyeşil et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Clement Godard et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Alexey Dosovitskiy et al.
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Markus Achtelik et al.
UNMANNED SYSTEMS TECHNOLOGY XI
(2009)
