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Artykuły w czasopismach na temat „Camera based Depth Estimation”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Lin, Bor-Shing, I.-Jung Lee, Chin-Shyurng Fahn, Yi-Fang Lee, Wei-Jen Chou, and Meng-Luen Wu. "Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm." Sensors 21, no. 12 (2021): 4216. http://dx.doi.org/10.3390/s21124216.

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Insufficient physical activity is common in modern society. By estimating the energy expenditure (EE) of different physical activities, people can develop suitable exercise plans to improve their lifestyle quality. However, several limitations still exist in the related works. Therefore, the aim of this study is to propose an accurate EE estimation model based on depth camera data with physical activity classification to solve the limitations in the previous research. To decide the best location and amount of cameras of the EE estimation, three depth cameras were set at three locations, namely

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Mansour, Mostafa, Pavel Davidson, Oleg Stepanov, and Robert Piché. "Relative Importance of Binocular Disparity and Motion Parallax for Depth Estimation: A Computer Vision Approach." Remote Sensing 11, no. 17 (2019): 1990. http://dx.doi.org/10.3390/rs11171990.

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Binocular disparity and motion parallax are the most important cues for depth estimation in human and computer vision. Here, we present an experimental study to evaluate the accuracy of these two cues in depth estimation to stationary objects in a static environment. Depth estimation via binocular disparity is most commonly implemented using stereo vision, which uses images from two or more cameras to triangulate and estimate distances. We use a commercial stereo camera mounted on a wheeled robot to create a depth map of the environment. The sequence of images obtained by one of these two came

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Furmonas, Justas, John Liobe, and Vaidotas Barzdenas. "Analytical Review of Event-Based Camera Depth Estimation Methods and Systems." Sensors 22, no. 3 (2022): 1201. http://dx.doi.org/10.3390/s22031201.

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Event-based cameras have increasingly become more commonplace in the commercial space as the performance of these cameras has also continued to increase to the degree where they can exponentially outperform their frame-based counterparts in many applications. However, instantiations of event-based cameras for depth estimation are sparse. After a short introduction detailing the salient differences and features of an event-based camera compared to that of a traditional, frame-based one, this work summarizes the published event-based methods and systems known to date. An analytical review of the

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Son, Eunjin, Jiho Choi, Jimin Song, Yongsik Jin, and Sang Jun Lee. "Monocular Depth Estimation from a Fisheye Camera Based on Knowledge Distillation." Sensors 23, no. 24 (2023): 9866. http://dx.doi.org/10.3390/s23249866.

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Monocular depth estimation is a task aimed at predicting pixel-level distances from a single RGB image. This task holds significance in various applications including autonomous driving and robotics. In particular, the recognition of surrounding environments is important to avoid collisions during autonomous parking. Fisheye cameras are adequate to acquire visual information from a wide field of view, reducing blind spots and preventing potential collisions. While there have been increasing demands for fisheye cameras in visual-recognition systems, existing research on depth estimation has pri

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Seo, Jonghyun, and Jangmyung Lee. "Object-Based Visual Servoing for Autonomous Mobile Manipulators." International Journal of Humanoid Robotics 11, no. 03 (2014): 1450025. http://dx.doi.org/10.1142/s021984361450025x.

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This paper proposes an object based vision (O-BV) system to implement visual servoing for autonomous mobile manipulators using two charge-coupled device (CCD) cameras. Conventional stereo vision (C-SV) system estimates the depth based on the disparity between two camera images for the same object. However, the disparity is not an effective cue for a small disparity at a long distance. To resolve this problem, in the proposed O-BV system, the individual camera tracks the object independently, and the angles of the two cameras are used to estimate the distance to the object. This depth estimatio

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Zaman, DMS, Md Hasan Maruf, Md Ashiqur Rahman, Jannatul Ferdousy, and ASM Shihavuddin. "Food Depth Estimation Using Low-Cost Mobile-Based System for Real-Time Dietary Assessment." GUB Journal of Science and Engineering 6, no. 1 (2020): 1–11. http://dx.doi.org/10.3329/gubjse.v6i1.52044.

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Real time estimation of nutrition intake from regular food items using mobile-based applications could be a breakthrough in creating public awareness of threats in overeating or faulty food choices. The bottleneck in implementing such systems is to effectively estimate the depths of the food items which is essential to calculate the volumes of foods. Volumes and density of food items can be used to estimate the weights of food eaten and their corresponding nutrition contents. Without specific depth sensors, it is very difficult to estimate the depth of any object from a single camera. Such sen

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Shimada, Tomoyasu, Hiroki Nishikawa, Xiangbo Kong, and Hiroyuki Tomiyama. "Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim." Sensors 22, no. 6 (2022): 2097. http://dx.doi.org/10.3390/s22062097.

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In this work, we propose a method for estimating depth for an image of a monocular camera in order to avoid a collision for the autonomous flight of a drone. The highest flight speed of a drone is generally approximate 22.2 m/s, and long-distant depth information is crucial for autonomous flights since if the long-distance information is not available, the drone flying at high speeds is prone to collisions. However, long-range, measurable depth cameras are too heavy to be equipped on a drone. This work applies Pix2Pix, which is a kind of Conditional Generative Adversarial Nets (CGAN). Pix2Pix

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Wu, Zhihong, Fuxiang Li, Yuan Zhu, Ke Lu, and Mingzhi Wu. "Design of a Robust System Architecture for Tracking Vehicle on Highway Based on Monocular Camera." Sensors 22, no. 9 (2022): 3359. http://dx.doi.org/10.3390/s22093359.

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Multi-Target tracking is a central aspect of modeling the environment of autonomous vehicles. A mono camera is a necessary component in the autonomous driving system. One of the biggest advantages of the mono camera is it can give out the type of vehicle and cameras are the only sensors able to interpret 2D information such as road signs or lane markings. Besides this, it has the advantage of estimating the lateral velocity of the moving object. The mono camera is now being used by companies all over the world to build autonomous vehicles. In the expressway scenario, the forward-looking camera

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Ma, Lan, and Hua Huo. "2D to 3D Human Skeleton Estimation Based on the Brown Camera Distortion Model and Constrained Optimization." Electronics 14, no. 5 (2025): 960. https://doi.org/10.3390/electronics14050960.

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In the rapidly evolving field of computer vision and machine learning, 3D skeleton estimation is critical for applications such as motion analysis and human–computer interaction. While stereo cameras are commonly used to acquire 3D skeletal data, monocular RGB systems attract attention due to benefits including cost-effectiveness and simple deployment. However, persistent challenges remain in accurately inferring depth from 2D images and reconstructing 3D structures using monocular approaches. The current 2D to 3D skeleton estimation methods overly rely on deep training of datasets, while negl

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Kaichi, Tomoya, Tsubasa Maruyama, Mitsunori Tada, and Hideo Saito. "Resolving Position Ambiguity of IMU-Based Human Pose with a Single RGB Camera." Sensors 20, no. 19 (2020): 5453. http://dx.doi.org/10.3390/s20195453.

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Human motion capture (MoCap) plays a key role in healthcare and human–robot collaboration. Some researchers have combined orientation measurements from inertial measurement units (IMUs) and positional inference from cameras to reconstruct the 3D human motion. Their works utilize multiple cameras or depth sensors to localize the human in three dimensions. Such multiple cameras are not always available in our daily life, but just a single camera attached in a smart IP devices has recently been popular. Therefore, we present a 3D pose estimation approach from IMUs and a single camera. In order to

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Roberts, William A., and Brian S. Thurow. "Correlation-Based Depth Estimation with a Plenoptic Camera." AIAA Journal 55, no. 2 (2017): 435–45. http://dx.doi.org/10.2514/1.j055050.

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Sun, Manhui, Shaowu Yang, and Hengzhu Liu. "Convolutional neural network-based coarse initial position estimation of a monocular camera in large-scale 3D light detection and ranging maps." International Journal of Advanced Robotic Systems 16, no. 6 (2019): 172988141989351. http://dx.doi.org/10.1177/1729881419893518.

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Initial position estimation in global maps, which is a prerequisite for accurate localization, plays a critical role in mobile robot navigation tasks. Global positioning system signals often become unreliable in disaster sites or indoor areas, which require other localization methods to help the robot in searching and rescuing. Many visual-based approaches focus on estimating a robot’s position within prior maps acquired with cameras. In contrast to conventional methods that need a coarse estimation of initial position to precisely localize a camera in a given map, we propose a novel approach

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El Kaid, Amal, Denis Brazey, Vincent Barra, and Karim Baïna. "Top-Down System for Multi-Person 3D Absolute Pose Estimation from Monocular Videos." Sensors 22, no. 11 (2022): 4109. http://dx.doi.org/10.3390/s22114109.

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Two-dimensional (2D) multi-person pose estimation and three-dimensional (3D) root-relative pose estimation from a monocular RGB camera have made significant progress recently. Yet, real-world applications require depth estimations and the ability to determine the distances between people in a scene. Therefore, it is necessary to recover the 3D absolute poses of several people. However, this is still a challenge when using cameras from single points of view. Furthermore, the previously proposed systems typically required a significant amount of resources and memory. To overcome these restrictio

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Zeller, N., C. A. Noury, F. Quint, C. Teulière, U. Stilla, and M. Dhome. "METRIC CALIBRATION OF A FOCUSED PLENOPTIC CAMERA BASED ON A 3D CALIBRATION TARGET." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 449–56. http://dx.doi.org/10.5194/isprsannals-iii-3-449-2016.

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In this paper we present a new calibration approach for focused plenoptic cameras. We derive a new mathematical projection model of a focused plenoptic camera which considers lateral as well as depth distortion. Therefore, we derive a new depth distortion model directly from the theory of depth estimation in a focused plenoptic camera. In total the model consists of five intrinsic parameters, the parameters for radial and tangential distortion in the image plane and two new depth distortion parameters. In the proposed calibration we perform a complete bundle adjustment based on a 3D calibratio

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Zeller, N., C. A. Noury, F. Quint, C. Teulière, U. Stilla, and M. Dhome. "METRIC CALIBRATION OF A FOCUSED PLENOPTIC CAMERA BASED ON A 3D CALIBRATION TARGET." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 449–56. http://dx.doi.org/10.5194/isprs-annals-iii-3-449-2016.

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In this paper we present a new calibration approach for focused plenoptic cameras. We derive a new mathematical projection model of a focused plenoptic camera which considers lateral as well as depth distortion. Therefore, we derive a new depth distortion model directly from the theory of depth estimation in a focused plenoptic camera. In total the model consists of five intrinsic parameters, the parameters for radial and tangential distortion in the image plane and two new depth distortion parameters. In the proposed calibration we perform a complete bundle adjustment based on a 3D calibratio

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Li, Yinhao, Zheng Ge, Guanyi Yu, et al. "BEVDepth: Acquisition of Reliable Depth for Multi-View 3D Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1477–85. http://dx.doi.org/10.1609/aaai.v37i2.25233.

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In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work is based on a key observation -- depth estimation in recent approaches is surprisingly inadequate given the fact that depth is essential to camera 3D detection. Our BEVDepth resolves this by leveraging explicit depth supervision. A camera-awareness depth estimation module is also introduced to facilitate the depth predicting capability. Besides, we design a novel Depth Refinement Module to counter the side effects carri

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Tang, Jingyuan, Xiao Zhang, Peter Kok-Yiu Wong, and Jack C. P. Cheng. "Method on pose estimation of excavators based on onboard depth camera." IOP Conference Series: Earth and Environmental Science 1101, no. 7 (2022): 072005. http://dx.doi.org/10.1088/1755-1315/1101/7/072005.

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Abstract Pose estimation of construction machines describes their motions and orientations in 3D space, which provides basic information for remote control, safety monitoring, and productivity analysis. Among the commonly used construction machines, the excavator is an important one whose pose information has significant value in the development of automatic driving systems and operation safety monitoring. Regarding vision-based pose estimation of excavators, previous studies mainly rely on image data from digital cameras installed on sites. However, their robustness may drop due to occlusions

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Xu, Chao, Guoxu Li, Ye Bai, Yuzhuo Bai, Zheng Cao, and Cheng Han. "Tracking and Registration Technology Based on Panoramic Cameras." Applied Sciences 15, no. 13 (2025): 7397. https://doi.org/10.3390/app15137397.

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Augmented reality (AR) has become a research focus in computer vision and graphics, with growing applications driven by advances in artificial intelligence and the emergence of the metaverse. Panoramic cameras offer new opportunities for AR due to their wide field of view but also pose significant challenges for camera pose estimation because of severe distortion and complex scene textures. To address these issues, this paper proposes a lightweight, unsupervised deep learning model for panoramic camera pose estimation. The model consists of a depth estimation sub-network and a pose estimation

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Izadmehr, Yasaman, Héctor F. Satizábal, Kamiar Aminian, and Andres Perez-Uribe. "Depth Estimation for Egocentric Rehabilitation Monitoring Using Deep Learning Algorithms." Applied Sciences 12, no. 13 (2022): 6578. http://dx.doi.org/10.3390/app12136578.

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Upper limb impairment is one of the most common problems for people with neurological disabilities, affecting their activity, quality of life (QOL), and independence. Objective assessment of upper limb performance is a promising way to help patients with neurological upper limb disorders. By using wearable sensors, such as an egocentric camera, it is possible to monitor and objectively assess patients’ actual performance in activities of daily life (ADLs). We analyzed the possibility of using Deep Learning models for depth estimation based on a single RGB image to allow the monitoring of patie

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Zhang, Fan, Junli Zhao, Liang Wang, and Fuqing Duan. "3D Face Model Super-Resolution Based on Radial Curve Estimation." Applied Sciences 10, no. 3 (2020): 1047. http://dx.doi.org/10.3390/app10031047.

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Consumer depth cameras bring about cheap and fast acquisition of 3D models. However, the precision and resolution of these consumer depth cameras cannot satisfy the requirements of some 3D face applications. In this paper, we present a super-resolution method for reconstructing a high resolution 3D face model from a low resolution 3D face model acquired from a consumer depth camera. We used a group of radial curves to represent a 3D face. For a given low resolution 3D face model, we first extracted radial curves on it, and then estimated their corresponding high resolution ones by radial curve

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Lee, Seungyoo, Kyujin Han, Seonyeong Park, and Xiaopeng Yang. "Vehicle Distance Estimation from a Monocular Camera for Advanced Driver Assistance Systems." Symmetry 14, no. 12 (2022): 2657. http://dx.doi.org/10.3390/sym14122657.

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The purpose of this study is to propose a framework for accurate and efficient vehicle distance estimation from a monocular camera. The proposed framework consists of a transformer-based object detector, a transformer-based depth estimator, and a distance predictor. The object detector detects various objects that are mostly symmetrical from an image captured by the monocular camera and provides the type of each object and the coordinate information of a bounding box around each object. The depth estimator generates a depth map for the image. Then, the bounding boxes are overlapped with the de

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Kim, Ju-Young, In-Seon Kim, Dai-Yeol Yun, Tae-Won Jung, Soon-Chul Kwon, and Kye-Dong Jung. "Visual Positioning System Based on 6D Object Pose Estimation Using Mobile Web." Electronics 11, no. 6 (2022): 865. http://dx.doi.org/10.3390/electronics11060865.

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Recently, the demand for location-based services using mobile devices in indoor spaces without a global positioning system (GPS) has increased. However, to the best of our knowledge, solutions that are fully applicable to indoor positioning and navigation and ensure real-time mobility on mobile devices, such as global navigation satellite system (GNSS) solutions, cannot achieve remarkable researches in indoor circumstances. Indoor single-shot image positioning using smartphone cameras does not require a dedicated infrastructure and offers the advantages of low price and large potential markets

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Cai-Wen Niu, Cai-Wen Niu, Shi-Hui Dong Cai-Wen Niu, and Yan Zeng Shi-Hui Dong. "Research on Improved Algorithm of Mobile Robot Vision SLAM Based on Depth Camera." 電腦學刊 33, no. 5 (2022): 197–211. http://dx.doi.org/10.53106/199115992022103305017.

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<p>Aiming at the problems of low accuracy, poor real-time performance and large cumulative error in traditional depth camera visual synchronization positioning and map construction algorithms, this paper proposes an improved visual SLAM algorithm for mobile robots based on depth camera. According to the realization process of robot vision, a feature point extraction algorithm based on feature extraction and image segmentation watershed algorithm is proposed in the front-end process, which improves the real-time performance of the algorithm. Then the RE-RANSAC algorithm is used to elimina

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Choi, Youn-Ho, and Seok-Cheol Kee. "Monocular Depth Estimation Using a Laplacian Image Pyramid with Local Planar Guidance Layers." Sensors 23, no. 2 (2023): 845. http://dx.doi.org/10.3390/s23020845.

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It is important to estimate the exact depth from 2D images, and many studies have been conducted for a long period of time to solve depth estimation problems. Recently, as research on estimating depth from monocular camera images based on deep learning is progressing, research for estimating accurate depths using various techniques is being conducted. However, depth estimation from 2D images has been a problem in predicting the boundary between objects. In this paper, we aim to predict sophisticated depths by emphasizing the precise boundaries between objects. We propose a depth estimation net

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Liu, Xin, Egor Bondarev, Sander R. Klomp, Joury Zimmerman, and Peter H. N. de With. "Semantic 3D Indoor Reconstruction with Stereo Camera Imaging." Electronic Imaging 2021, no. 18 (2021): 105–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.18.3dia-105.

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On-the-fly reconstruction of 3D indoor environments has recently become an important research field to provide situational awareness for first responders, like police and defence officers. The protocols do not allow deployment of active sensors (LiDAR, ToF, IR cameras) to prevent the danger of being exposed. Therefore, passive sensors, such as stereo cameras or moving mono sensors, are the only viable options for 3D reconstruction. At present, even the best portable stereo cameras provide an inaccurate estimation of depth images, caused by the small camera baseline. Reconstructing a complete s

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Jiang, Guolai, Shaokun Jin, Yongsheng Ou, and Shoujun Zhou. "Depth Estimation of a Deformable Object via a Monocular Camera." Applied Sciences 9, no. 7 (2019): 1366. http://dx.doi.org/10.3390/app9071366.

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The depth estimation of the 3D deformable object has become increasingly crucial to various intelligent applications. In this paper, we propose a feature-based approach for accurate depth estimation of a deformable 3D object with a single camera, which reduces the problem of depth estimation to a pose estimation problem. The proposed method needs to reconstruct the target object at the very beginning. With the 3D reconstruction as an a priori model, only one monocular image is required afterwards to estimate the target object’s depth accurately, regardless of pose changes or deformability of t

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Jiao, Yang, Zequn Jie, Shaoxiang Chen, et al. "Instance-Aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2598–606. http://dx.doi.org/10.1609/aaai.v38i3.28037.

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Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. Under such a paradigm, accurate BEV representation construction relies on reliable depth estimation for multi-camera images. However, existing approaches exhaustively predict depths for every pixel without prioritizing objects, which are precisely the entities requiring detection in the 3D space. To this end, we propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector. First, a category-specific structural prior

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Seo, Beom-Su, Byungjae Park, and Hoon Choi. "Sensing Range Extension for Short-Baseline Stereo Camera Using Monocular Depth Estimation." Sensors 22, no. 12 (2022): 4605. http://dx.doi.org/10.3390/s22124605.

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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). To combine a stereo depth and a monocular depth, the proposed method estimates a scale factor of a monocular depth using stereo depth–mono depth pairs and then combines the two depths. Another advantage of the proposed method is that the trained MDE model may be utilized for different environments without retraining. The performance of the proposed me

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Jin, Young-Hoon, Kwang-Woo Ko, and Won-Hyung Lee. "An Indoor Location-Based Positioning System Using Stereo Vision with the Drone Camera." Mobile Information Systems 2018 (October 17, 2018): 1–13. http://dx.doi.org/10.1155/2018/5160543.

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Stereo vision is used to reconstruct 3D information of the space by estimating the depth value from the simulation of human eyes. Spatial restoration can be used as a means of location estimation in an indoor area, which is impossible to accomplish using the relative location estimation technology, GPS. By mapping the real world in virtual space, it is feasible to clear the boundary between real space and virtual space. This paper presents a method to control the drone indoors through a positioning system using Structure from Motion algorithm (SfM). SfM calculates the relative relationship bet

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Tran, Van Luan, and Huei-Yung Lin. "A Structured Light RGB-D Camera System for Accurate Depth Measurement." International Journal of Optics 2018 (November 1, 2018): 1–7. http://dx.doi.org/10.1155/2018/8659847.

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The ability to reliably measure the depth of the object surface is very important in a range of high-value industries. With the development of 3D vision techniques, RGB-D cameras have been widely used to perform the 6D pose estimation of target objects for a robotic manipulator. Many applications require accurate shape measurements of the objects for 3D template matching. In this work, we develop an RGB-D camera based on the structured light technique with gray-code coding. The intrinsic and extrinsic parameters of the camera system are determined by a calibration process. 3D reconstruction of

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Tripathee, Saman, Samyam Aryal, and Samiran Pratap Bibash. "Drishya: Object Detection And MonocuLar Depth Estimation Using Deep Learning." Journal of Advanced College of Engineering and Management 8, no. 2 (2023): 149–59. http://dx.doi.org/10.3126/jacem.v8i2.55951.

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This project focuses on object detection and depth estimation using computer vision. The aim of this project is to develop an accurate and reliable system for detecting objects and estimating their distances from a camera. The system uses deep learning-based algorithms to detect objects and estimate their distances. The results of the project show that the system can accurately detect objects and estimate their distances in some scenarios. This project presents an Android app for real-time object detection and depth estimation using computer vision with YOLOv5 and U-Net architecture.

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Galauskis, Maris, and Arturs Ardavs. "The Process of Data Validation and Formatting for an Event-Based Vision Dataset in Agricultural Environments." Applied Computer Systems 26, no. 2 (2021): 173–77. http://dx.doi.org/10.2478/acss-2021-0021.

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Abstract In this paper, we describe our team’s data processing practice for an event-based camera dataset. In addition to the event-based camera data, the Agri-EBV dataset contains data from LIDAR, RGB, depth cameras, temperature, moisture, and atmospheric pressure sensors. We describe data transfer from a platform, automatic and manual validation of data quality, conversions to multiple formats, and structuring of the final data. Accurate time offset estimation between sensors achieved in the dataset uses IMU data generated by purposeful movements of the sensor platform. Therefore, we also ou

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Hoseini, Seyyed, and Peyman Kabiri. "A Novel Feature-Based Approach for Indoor Monocular SLAM." Electronics 7, no. 11 (2018): 305. http://dx.doi.org/10.3390/electronics7110305.

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Camera tracking and the construction of a robust and accurate map in unknown environments are still challenging tasks in computer vision and robotic applications. Visual Simultaneous Localization and Mapping (SLAM) along with Augmented Reality (AR) are two important applications, and their performance is entirely dependent on the accuracy of the camera tracking routine. This paper presents a novel feature-based approach for the monocular SLAM problem using a hand-held camera in room-sized workspaces with a maximum scene depth of 4–5 m. In the core of the proposed method, there is a Particle Fi

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Fonder, Michaël, Damien Ernst, and Marc Van Droogenbroeck. "Parallax Inference for Robust Temporal Monocular Depth Estimation in Unstructured Environments." Sensors 22, no. 23 (2022): 9374. http://dx.doi.org/10.3390/s22239374.

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Estimating the distance to objects is crucial for autonomous vehicles, but cost, weight or power constraints sometimes prevent the use of dedicated depth sensors. In this case, the distance has to be estimated from on-board mounted RGB cameras, which is a complex task especially for environments such as natural outdoor landscapes. In this paper, we present a new depth estimation method suitable for use in such landscapes. First, we establish a bijective relationship between depth and the visual parallax of two consecutive frames and show how to exploit it to perform motion-invariant pixel-wise

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Korkalo, Otto, and Tapio Takala. "Measurement Noise Model for Depth Camera-Based People Tracking." Sensors 21, no. 13 (2021): 4488. http://dx.doi.org/10.3390/s21134488.

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Depth cameras are widely used in people tracking applications. They typically suffer from significant range measurement noise, which causes uncertainty in the detections made of the people. The data fusion, state estimation and data association tasks require that the measurement uncertainty is modelled, especially in multi-sensor systems. Measurement noise models for different kinds of depth sensors have been proposed, however, the existing approaches require manual calibration procedures which can be impractical to conduct in real-life scenarios. In this paper, we present a new measurement no

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Xie, Wentao, Chi Xu, Yanbin Gong, et al. "DeepBreath: Breathing Exercise Assessment with a Depth Camera." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 3 (2024): 1–26. http://dx.doi.org/10.1145/3678519.

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Practicing breathing exercises is crucial for patients with chronic obstructive pulmonary disease (COPD) to enhance lung function. Breathing mode (chest or belly breathing) and lung volume are two important metrics for supervising breathing exercises. Previous works propose that these metrics can be sensed separately in a contactless way, but they are impractical with unrealistic assumptions such as distinguishable chest and belly breathing patterns, the requirement of calibration, and the absence of body motions. In response, this research proposes DeepBreath, a novel depth camera-based breat

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He, Xiankang, Guangkai Xu, Bo Zhang, Hao Chen, Ying Cui, and Dongyan Guo. "DiffCalib: Reformulating Monocular Camera Calibration as Diffusion-Based Dense Incident Map Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 3428–36. https://doi.org/10.1609/aaai.v39i3.32355.

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Monocular camera calibration is a key precondition for numerous 3D vision applications. Despite considerable advancements, existing methods often hinge on specific assumptions and struggle to generalize across varied real-world scenarios, and the performance is limited by insufficient training data. Recently, diffusion models trained on expansive datasets have been confirmed to maintain the capability to generate diverse, high-quality images. This success suggests a strong potential of the models to effectively understand varied visual information. In this work, we leverage the comprehensive v

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Oh, KyeongTaek, Cheung Soo Shin, Jeongmin Kim, and Sun K. Yoo. "Level-Set Segmentation-Based Respiratory Volume Estimation Using a Depth Camera." IEEE Journal of Biomedical and Health Informatics 23, no. 4 (2019): 1674–82. http://dx.doi.org/10.1109/jbhi.2018.2870859.

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Hu, Guang, and Peifeng Sun. "Depth Estimation of Monocular VR Scenes based on Improved Attention Combined with Deep Neural Network Models." Scalable Computing: Practice and Experience 26, no. 3 (2025): 1529–38. https://doi.org/10.12694/scpe.v26i3.3919.

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The boundary blurring issue with the existing unsupervised monocular depth estimation techniques is addressed by a suggested network design based on a dual attention module. This architecture is able to overcome the boundary blurring issue in depth estimation by making effective use of the remote contextual information of picture features. The model framework comprises of a pose estimation network and a depth estimation network to estimate depth and camera pose transformations simultaneously. The complete framework is trained using an unsupervised method based on view synthesis. The depth esti

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Ravi, Ravi, A. N. .., Ahmed L. .., Ravi Sekhar, Pritesh Shah, and Jamal F. Tawfeq. "Improving Shape Transformations for RGB Cameras Using Photometric Stereo." Fusion: Practice and Applications 15, no. 1 (2024): 144–56. http://dx.doi.org/10.54216/fpa.150112.

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The emergence of low-cost red, green, and blue (RGB) cameras has significantly impacted various computer vision tasks. However, these cameras often produce depth maps with limited object details, noise, and missing information. These limitations can adversely affect the quality of 3D reconstruction and the accuracy of camera trajectory estimation. Additionally, existing depth refinement methods struggle to distinguish shape from complex albedo, leading to visible artifacts in the refined depth maps. In this paper, we address these challenges by proposing two novel methods based on the theory o

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Ayhan and Kwan. "Mastcam Image Resolution Enhancement with Application to Disparity Map Generation for Stereo Images with Different Resolutions." Sensors 19, no. 16 (2019): 3526. http://dx.doi.org/10.3390/s19163526.

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In this paper, we introduce an in-depth application of high-resolution disparity map estimation using stereo images from Mars Curiosity rover’s Mastcams, which have two imagers with different resolutions. The left Mastcam has three times lower resolution as that of the right. The left Mastcam image’s resolution is first enhanced with three methods: Bicubic interpolation, pansharpening-based method, and a deep learning super resolution method. The enhanced left camera image and the right camera image are then used to estimate the disparity map. The impact of the left camera image enhancement is

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Ban, Yuxi, Mingzhe Liu, Peng Wu, et al. "Depth Estimation Method for Monocular Camera Defocus Images in Microscopic Scenes." Electronics 11, no. 13 (2022): 2012. http://dx.doi.org/10.3390/electronics11132012.

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When using a monocular camera for detection or observation, one only obtain two-dimensional information, which is far from adequate for surgical robot manipulation and workpiece detection. Therefore, at this scale, obtaining three-dimensional information of the observed object, especially the depth information estimation of the surface points of each object, has become a key issue. This paper proposes two methods to solve the problem of depth estimation of defiant images in microscopic scenes. These are the depth estimation method of the defocused image based on a Markov random field, and the

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Gongye, Qingxuan, Peng Cheng, and Jiuxiang Dong. "Image-based visual servoing with depth estimation." Transactions of the Institute of Measurement and Control 44, no. 9 (2021): 1811–23. http://dx.doi.org/10.1177/01423312211064681.

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For the depth estimation problem in the image-based visual servoing (IBVS) control, this paper proposes a new observer structure based on Kalman filter (KF) to recover the feature depth in real time. First, according to the number of states, two different mathematical models of the system are established. The first one is to extract the depth information from the Jacobian matrix as the state vector of the system. The other is to use the depth information and the coordinate point information of the two-dimensional image plane as the state vector of the system. The KF is used to estimate the unk

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Zhao, Kun, Yuhuan Li, and Zunmin Liu. "Three-Dimensional Spatial Perception of Blueberry Fruits Based on Improved YOLOv11 Network." Agronomy 15, no. 3 (2025): 535. https://doi.org/10.3390/agronomy15030535.

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The automated harvesting of blueberries using a picking robot places a greater demand on the 3D spatial perception performance, as the robot’s grasping mechanism needs to pick blueberry fruits accurately at specific positions and in particular poses. To achieve this goal, this paper presents a method for blueberry detection, 3D spatial localization, and pose estimation using visual perception, which can be deployed on an OAK depth camera. Firstly, a blueberry and calyx scar detection dataset is constructed to train the detection network and evaluate its performance. Secondly, the blueberry and

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Shi, Lei. "An Object Detection and Pose Estimation Approach for Position Based Visual Servoing." Electrical, Control and Communication Engineering 12, no. 1 (2017): 34–39. http://dx.doi.org/10.1515/ecce-2017-0005.

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Abstract In this paper, an object recognition method and a pose estimation approach using stereo vision is presented. The proposed approach was used for position based visual servoing of a 6 DoF manipulator. The object detection and recognition method was designed with the purpose of increasing robustness. A RGB color-based object descriptor and an online correction method is proposed for object detection and recognition. Pose was estimated by using the depth information derived from stereo vision camera and an SVD based method. Transformation between the desired pose and object pose was calcu

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Liang, Hong, Zizhen Ma, and Qian Zhang. "Self-Supervised Object Distance Estimation Using a Monocular Camera." Sensors 22, no. 8 (2022): 2936. http://dx.doi.org/10.3390/s22082936.

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Distance estimation using a monocular camera is one of the most classic tasks for computer vision. Current monocular distance estimating methods need a lot of data collection or they produce imprecise results. In this paper, we propose a network for both object detection and distance estimation. A network-based on ShuffleNet and YOLO is used to detect an object, and a self-supervised learning network is used to estimate distance. We calibrated the camera, and the calibrated parameters were integrated into the overall network. We also analyzed the parameter variation of the camera pose. Further

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Zhi, Henghui, Chenyang Yin, Huibin Li, and Shanmin Pang. "An Unsupervised Monocular Visual Odometry Based on Multi-Scale Modeling." Sensors 22, no. 14 (2022): 5193. http://dx.doi.org/10.3390/s22145193.

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Unsupervised deep learning methods have shown great success in jointly estimating camera pose and depth from monocular videos. However, previous methods mostly ignore the importance of multi-scale information, which is crucial for pose estimation and depth estimation, especially when the motion pattern is changed. This article proposes an unsupervised framework for monocular visual odometry (VO) that can model multi-scale information. The proposed method utilizes densely linked atrous convolutions to increase the receptive field size without losing image information, and adopts a non-local sel

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Park, Sangmi, Changhee Hong, Inkyu Hwang, and Jaewook Lee. "Comparison of Single-Camera-Based Depth Estimation Technology for Digital Twin Model Synchronization of Underground Utility Tunnels." Applied Sciences 13, no. 4 (2023): 2106. http://dx.doi.org/10.3390/app13042106.

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Digital twin technology can be used for disaster safety management of underground utility tunnels, which are social infrastructures. To verify its efficiency in an underground utility tunnel, it must be implemented and monitored in the real world. Therefore, the state of information about an object and the position of an object in the real world, which can change upon movement, should be correspondingly reflected in the digital model. Underground utility tunnels are monitored through closed circuit television (CCTV) cameras because the main infrastructure facilities are installed in a long and

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Jun, Jinyoung, Jae-Han Lee, and Chang-Su Kim. "Versatile depth estimator based on common relative depth estimation and camera-specific relative-to-metric depth conversion." Journal of Visual Communication and Image Representation 103 (August 2024): 104252. http://dx.doi.org/10.1016/j.jvcir.2024.104252.

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Liu, Wenlei, Sentang Wu, Zhongbo Wu, and Xiaolong Wu. "Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation." Sensors 19, no. 22 (2019): 4945. http://dx.doi.org/10.3390/s19224945.

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The novel contribution of this paper is to propose an incremental pose map optimization for monocular vision simultaneous localization and mapping (SLAM) based on similarity transformation, which can effectively solve the scale drift problem of SLAM for monocular vision and eliminate the cumulative error by global optimization. With the method of mixed inverse depth estimation based on a probability graph, the problem of the uncertainty of depth estimation is effectively solved and the robustness of depth estimation is improved. Firstly, this paper proposes a method combining the sparse direct

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