Results 41 to 50 of about 3,462,291 (207)
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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Qualitative Estimation of Depth in Monocular Vision [PDF]
, 2001 In this paper we propose two techniques to qualitatively estimate distance in monocular vision. Two kinds of approaches are described, the former based on texture analysis and the latter on histogram inspection. Although both the methods allow only to determine whether a point within an image is nearer or farther than another with respect to the ...
CANTONI, VIRGINIO +3 more
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Aperture Supervision for Monocular Depth Estimation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint ...
Pratul P. Srinivasan +4 more
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On the Uncertainty of Self-Supervised Monocular Depth Estimation [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 CVPR 2020.
M. Poggi +3 more
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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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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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FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume [PDF]
, 2023 Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces.
Liu, Jianlin +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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