Self-supervised monocular depth estimation can exhibit excellent performance in static environments due to the multi-view consistency assumption during the training process. However, it is hard to maintain depth consistency in dynamic scenes when con...
In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised me...
Due to the existence of low-textured areas in indoor scenes, some self-supervised depth estimation methods have specifically designed sparse photometric consistency losses and geometry-based losses. However, some of the loss terms cannot supervise al...
This paper presents a new deep visual-inertial odometry and depth estimation framework for improving the accuracy of depth estimation and ego-motion from image sequences and inertial measurement unit (IMU) raw data. The proposed framework predicts eg...
Self-supervised monocular depth estimation methods have become the focus of research since ground truth data are not required. Current single-image-based works only leverage appearance-based features, thus achieving a limited performance. Deep learni...
Self-supervised monocular depth estimation, which has attained remarkable progress for outdoor scenes in recent years, often faces greater challenges for indoor scenes. These challenges comprise: (i) non-textured regions: indoor scenes often contain...
To find an economical solution to infer the depth of the surrounding environment of unmanned agricultural vehicles (UAV), a lightweight depth estimation model called MonoDA based on a convolutional neural network is proposed. A series of sequential f...
Depth estimation algorithms are widely applied in various fields, including 3D reconstruction, autonomous driving, and industrial robotics. Monocular self-supervised algorithms for depth prediction offer a cost-effective alternative to acquiring dept...
Self-supervised monocular depth estimation from oblique UAV videos is crucial for enabling autonomous navigation and large-scale mapping. However, existing self-supervised monocular depth estimation methods face key challenges in UAV oblique video sc...
Existing self-supervised methods have achieved significant success in ground-level autonomous driving scenarios, but applying them directly to Unmanned Aerial Vehicle (UAV) videos remains challenging. On the one hand, rapid pose changes in UAVs often...
Recent studies on self-supervised monocular depth estimation have achieved promising results, which are mainly based on the joint optimization of depth and pose estimation via high-level photometric loss. However, how to learn the latent and benefici...
In recent studies, self-supervised learning methods have been explored for monocular depth estimation. They minimize the reconstruction loss of images instead of depth information as a supervised signal. However, existing methods usually assume that...
For UAVs, the industry currently relies on expensive sensors for obstacle avoidance. A significant challenge arises from the scarcity of high-quality depth estimation datasets tailored for low-altitude environments, which hinders the advancement of s...
Self-supervised monocular depth estimation is gaining significant attention because it can learn depth from video without needing expensive ground-truth data. However, many self-supervised models remain too heavy for edge devices, and simply shrinkin...
Autonomous driving represents a transformative advancement with the potential to significantly impact daily mobility, including enabling independent vehicle operation for individuals with visual disabilities. The commercialization of autonomous drivi...
Monocular depth estimation is an essential component in computer vision that enables 3D scene understanding, with critical applications in autonomous driving and augmented reality. This paper proposes a lightweight self-supervised framework from sing...
Self-supervised monocular depth estimation is challenging at night because adverse illumination degrades the visual cues required for correspondence estimation and depth inference. Although appearance compensation and domain transfer can mitigate nig...
Scene structure and local details are important factors in producing high-quality depth estimations so as to solve fuzzy artifacts in depth prediction results. We propose a new network structure that combines two channel attention modules in a deep p...
Accurate monocular depth estimation (MDE) is a foundational task in endoscopic surgery, critical for augmenting depth perception and aiding surgical navigation. While diffusion-based and discriminative depth estimators demonstrate complementary stren...
Monocular depth estimation provides low-cost environmental information for intelligent systems such as autonomous vehicles and robots, supporting sustainable development by reducing reliance on expensive, energy-intensive sensors and making technolog...
Monocular depth prediction research is essential for expanding meaning from 2D to 3D. Recent studies have focused on the application of a newly proposed encoder; however, the development within the self-supervised learning framework remains unexplore...
Estimating depth from a single low-altitude aerial image captured by an Unmanned Aerial System (UAS) has become a recent research focus. This method has a wide range of applications in 3D modeling, digital terrain models, and target detection. Tradit...
For the task of monocular depth estimation, self-supervised learning supervises training by calculating the pixel difference between the target image and the warped reference image, obtaining results comparable to those with full supervision. However...
Self-supervised monocular depth estimation has demonstrated high practical utility, as it can be trained using a photometric image reconstruction loss between the original image and a reprojected image generated from the estimated depth and relative...
Acquiring underwater depth maps is essential as they provide indispensable three-dimensional spatial information for visualizing the underwater environment. These depth maps serve various purposes, including underwater navigation, environmental monit...
The self-supervised monocular depth estimation paradigm has become an important branch of computer vision depth-estimation tasks. However, the depth estimation problem arising from object edge depth pulling or occlusion is still unsolved. The graysca...
Self-supervised depth estimation methods enable the recovery of scene depth information from monocular endoscopic images, thereby assisting endoscopic navigation. However, existing monocular endoscopic depth estimation methods generally fail to captu...
Thermal cameras are known for their ability to overcome lighting constraints and provide reliable thermal radiation images. This capability facilitates methods for depth and ego-motion estimation, enabling efficient learning of poses and scene struct...
A lot of research has been conducted in recent years on stereo depth estimation techniques, taking the traditional approach to a new level such that it is in an appreciably good form for competing in the depth estimation market with other methods, de...
Thermal infrared imaging is attracting much attention due to its strength against illuminance variation. However, because of the spectral difference between thermal infrared images and RGB images, the existing research on self-supervised monocular de...
In rainy and foggy conditions, the scattering of light and the occlusion effects of atmospheric particles distort the reflected light from object surfaces, leading to inconsistent depth information. As a result, depth estimation models trained under...
This paper introduces a novel approach for self-supervised monocular depth estimation. The model is trained on stereo–image (left–right pair) data and incorporates carefully designed perceptual image quality assessment-based loss function...
Monocular depth estimation (MDE) is a critical task in computer vision with applications in autonomous driving, robotics, and augmented reality. However, predicting depth from a single image poses significant challenges, especially in dynamic scenes...
Self-supervised monocular depth estimation (MDE) has emerged as a cost-effective alternative to traditional depth sensing approaches, which often require expensive equipment or complex configurations. However, these models often fail to maintain high...
Studies have shown that the observed image texture details and semantic information are of great significance for the depth estimation on the road scenes. However, there are ambiguities and inaccuracies in the boundary information of observed objects...
In the process of environmental perception, traditional CNN is often unable to effectively capture global context information due to its network structure, which leads to the problem of blurred edges of objects and scenes. Aiming at this problem, a s...
Real-time estimation of the safety distance between power operation workers and energized equipment is essential for preventing electric shock, arc flash injury, and equipment accidents during live or near-energized operations. However, large-scale d...
To improve the accuracy of using deep neural networks to predict the depth information of a single image, we proposed an unsupervised convolutional neural network for single-image depth estimation. Firstly, the network is improved by introducing a de...
Monocular depth estimation (MDE), as one of the fundamental tasks of computer vision, plays important roles in downstream applications such as virtual reality, 3D reconstruction, and robotic navigation. Convolutional neural networks (CNN)-based metho...
Recently, self-supervised multi-view stereo (MVS) methods, which are dependent primarily on optimizing networks using photometric consistency, have made clear progress. However, the difference in lighting between different views and reflective object...
Monocular depth estimation is a fundamental task with broad applications in autonomous driving and augmented reality. While recent lightweight methods achieve impressive performance, they often neglect the interaction of mid-order semantic features,...
Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain a real-time pseudo point cloud from imag...
Supervised monocular depth estimation methods based on learning have shown promising results compared with the traditional methods. However, these methods require a large number of high-quality corresponding ground truth depth data as supervision lab...
Monocular fisheye camera distance estimation is a crucial visual perception task for autonomous driving. Due to the practical challenges of acquiring precise depth annotations, existing self-supervised methods usually consist of a monocular distance...
Self-supervised depth estimation from monocular image sequences provides depth information without costly sensors like LiDAR, offering significant value for autonomous driving. Although self-supervised algorithms can reduce the dependence on labeled...
Self-supervised monocular visual odometry (VO) commonly relies on photometric consistency and rigid-scene assumptions to learn camera motion, making pose estimation vulnerable to unreliable cross-frame observations caused by limited geometric observa...
Despite the in-depth understanding of the synthetic aperture-radar (SAR) speckle and its characteristics, despeckling remains an open issue far from being solved. Deep-learning methods with supervised training have made great progress. However, relia...
Graph representation learning has become a mainstream method for processing network structured data, and most graph representation learning methods rely heavily on labeling information for downstream tasks. Since labeled information is rare in the re...
We propose a completely unsupervised approach to simultaneously estimate scene depth, ego-pose, ground segmentation and ground normal vector from only monocular RGB video sequences. In our approach, estimation for different scene structures can mutua...
A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be removed through the scope during a colonoscopy. Colorectal cancer can be prevented through this. However,...
of 2