Results 31 to 40 of about 744 (143)
Learn to Adapt for Monocular Depth Estimation
CoRR, 2022 Monocular depth estimation is one of the fundamental tasks in environmental perception and has achieved tremendous progress in virtue of deep learning. However, the performance of trained models tends to degrade or deteriorate when employed on other new datasets due to the gap between different datasets.
Qiyu Sun +3 more
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Adversarial Attacks on Monocular Depth Estimation
CoRR, 2020 Recent advances of deep learning have brought exceptional performance on many computer vision tasks such as semantic segmentation and depth estimation. However, the vulnerability of deep neural networks towards adversarial examples have caused grave concerns for real-world deployment.
Ziqi Zhang +4 more
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Sensing Range Extension for Short-Baseline Stereo Camera Using Monocular Depth Estimation
Sensors, 2022 This paper proposes a method to extend a sensing range of a short-baseline stereo camera (SBSC). The proposed method combines a stereo depth and a monocular depth estimated by a convolutional neural network-based monocular depth estimation (MDE).
Beom-Su Seo, Byungjae Park, Hoon Choi
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Remote Sensing, 2022 Monocular depth estimation is a fundamental yet challenging task in computer vision as depth information will be lost when 3D scenes are mapped to 2D images.
Songnan Chen +4 more
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LUMDE: Light-Weight Unsupervised Monocular Depth Estimation via Knowledge Distillation
Applied Sciences, 2022 The use of the unsupervised monocular depth estimation network approach has seen rapid progress in recent years, as it avoids the use of ground truth data, and also because monocular cameras are readily available in most autonomous devices. Although some
Wenze Hu +3 more
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Depth-Relative Self Attention for Monocular Depth Estimation
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023 Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on various visual hints such as size, shade, and texture extracted from RGB information. However, we observe that if such hints are overly exploited, the network can be biased on RGB
Kyuhong Shim +3 more
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DeepFusion encoder for unsupervised monocular metric depth estimation [PDF]
PeerJ Computer ScienceMonocular depth estimation is a fundamental task in computer vision, with significant applications in autonomous driving and robotics. However, accurately estimating depth from a single image remains challenging due to the absence of direct depth cues ...
Zhiwei Huang +6 more
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Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion
IEEE Access, 2021 Monocular depth estimation is a basic task in machine vision. In recent years, the performance of monocular depth estimation has been greatly improved. However, most depth estimation networks are based on a very deep network to extract features that lead
Xin Yang +4 more
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Monocular Depth Estimators: Vulnerabilities and Attacks
CoRR, 2020 Recent advancements of neural networks lead to reliable monocular depth estimation. Monocular depth estimated techniques have the upper hand over traditional depth estimation techniques as it only needs one image during inference. Depth estimation is one of the essential tasks in robotics, and monocular depth estimation has a wide variety of safety ...
Alwyn Mathew +2 more
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