Depth estimation from monocular images is an important task in localization and 3D reconstruction pipelines for bronchoscopic navigation. Various supervised self-supervised deep learning-based approaches have proven themselves on this natural images. However, the lack of labeled data bronchial tissue’s feature-scarce texture make utilization these methods ineffective scenes. In work, we propose...

Focus-based methods have shown promising results for the task of depth estimation in recent years. However, most existing focus-based approaches depend on maximal sharpness focal stack. These ignore spatial relationship between slices. The problem information loss caused by out-of-focus areas stack poses challenges this task. In paper, we propose a dynamically multi-modal learning strategy whic...

In computer vision, monocular depth estimation is the problem of obtaining a high-quality map from two-dimensional image. This provides information on three-dimensional scene geometry, which necessary for various applications in academia and industry, such as robotics autonomous driving. Recent studies based convolutional neural networks achieved impressive results this task. However, most prev...

Thermal infrared imaging is attracting much attention due to its strength against illuminance variation. However, because of the spectral difference between thermal images and RGB images, existing research on self-supervised monocular depth estimation has performance limitations. Therefore, in this study, we propose a novel Self-Guided Framework using Pseudolabel predicted from images. Our prop...

Depth estimation is a computer vision technique that critical for autonomous schemes sensing their surroundings and predict own condition. Traditional estimating approaches, such as structure from motion besides stereo similarity, rely on feature communications several views to provide depth information. In the meantime, maps anticipated are scarce. Gathering information via monocular an ill-po...

Learning depth from spherical panoramas is becoming a popular research topic because panorama has full field-of-view of the environment and provides relatively complete description scene. However, applying well-studied CNNs for perspective images to standard representation panoramas, i.e., equirectangular projection, suboptimal, as it becomes distorted towards poles. Another cubemap which disto...

Abstract Real-time estimation of actual object depth is an essential module for various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment. During the last decade machine learning, extensive deployment deep learning methods to computer vision has yielded approaches that succeed in achieving realistic synthesis out a simple RGB modality. Most these mo...

Visibility in poor weather condition is severely degraded by scattering of light due to suspended particles in the atmosphere such as haze and fog. In this paper, we propose defogging method from a single image based on depth estimation using blur. Formation of fog is the function of the depth. Estimation of depth information is under constraint problem if single image is available. Hence, remo...