Automatic Camera Pose Estimation by Key-Point Matching of Reference Objects DOI Open Access
Jinchen Zeng,

R Bütler,

John J. van den Dobbelsteen

и другие.

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Год журнала: 2023, Номер unknown, С. 1 - 5

Опубликована: Май 5, 2023

In this paper, we aim to design an automatic camera pose estimation pipeline for clinical spaces such as catheterization laboratories. Our proposed exploits Scaled-YOLOv4 detect fixed objects. We adopt the self-supervised key-point detector SuperPoint in combination with SuperGlue, a keypoint matching technique based on graph neural networks. Thus, match key-points input images annotated reference points. Reference points are chosen objects scene, corners of door posts or windows. The point-correspondences between image coordinates and 3D applied Perspective-n-Point algorithm estimate each camera. Compared other methods, does not require construction point-cloud model scene placing polyhedron object before required calibration. Using videos from real procedures, show that can high accuracy.

Язык: Английский

A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments DOI Creative Commons
Muhammad S. Alam, Farhan Mohamed, Ali Selamat

и другие.

IEEE Access, Год журнала: 2023, Номер 11, С. 43985 - 44009

Опубликована: Янв. 1, 2023

Camera localization involves the estimation of camera pose an image from a random scene. We used single or sequence images videos as input. The output depends on representation scene and method used. Several computer vision applications, such robot navigation safety inspection, can benefit localization. is to determine position object in containing multiple sequence. Structure-based techniques have achieved considerable success owing combination matching coordinate regression. Absolute relative regression provide end-to-end learning; however, they exhibit poor accuracies. Despite rapid growth vision, there has been no thorough review categorization, evaluation, synthesis structures regression-based techniques. Input format loss strategies for recurrent neural networks (RNN) not adequately described literature. main topic indoor regression, which part First, we discuss certain application areas then different techniques, feature structure-based, absolute simultaneous mapping (SLAM). evaluated frequently datasets qualitatively compared approaches. Finally, potential directions future research, optimizing computational cost features evaluating characteristics cameras.

Язык: Английский

Процитировано

27
An Assessment Towards 2D and 3D Human Pose Estimation and its Applications to Activity Recognition: A Review DOI
Pratishtha Verma, Rajeev Srivastava, Santosh Kumar Tripathy

и другие.

SN Computer Science, Год журнала: 2025, Номер 6(2)

Опубликована: Фев. 17, 2025

Язык: Английский

Процитировано

0
TIPAngle: Traffic Tracking at City Scale by Pose Estimation of Pan and Tilt Intersection Cameras DOI

S. N. Jagadeesha,

Edward Andert, Aviral Shrivastava

и другие.

Опубликована: Янв. 4, 2025

Язык: Английский

Процитировано

0
A review on 3D Gaussian splatting for sparse view reconstruction DOI Creative Commons
Haitian Liu,

Binglin Liu,

Qianchao Hu

и другие.

Artificial Intelligence Review, Год журнала: 2025, Номер 58(7)

Опубликована: Апрель 22, 2025

Язык: Английский

Процитировано

0
MDA-YOLO Person: a 2D human pose estimation model based on YOLO detection framework DOI
Chengang Dong, Yuhao Tang, Liyan Zhang

и другие.

Cluster Computing, Год журнала: 2024, Номер unknown

Опубликована: Июнь 11, 2024

Язык: Английский

Процитировано

2
Fusing structure from motion and simulation-augmented pose regression from optical flow for challenging indoor environments DOI Creative Commons
Felix Ott, Lucas Heublein, David Rügamer

и другие.

Journal of Visual Communication and Image Representation, Год журнала: 2024, Номер 103, С. 104256 - 104256

Опубликована: Авг. 1, 2024

The localization of objects is essential in many applications, such as robotics, virtual and augmented reality, warehouse logistics. Recent advancements deep learning have enabled using monocular cameras. Traditionally, structure from motion (SfM) techniques predict an object's absolute position a point cloud, while pose regression (APR) methods use neural networks to understand the environment semantically. However, both approaches face challenges environmental factors like blur, lighting changes, repetitive patterns, featureless areas. This study addresses these by incorporating additional information refining estimates with relative (RPR) methods. RPR also struggles issues blur. To overcome this, we compute optical flow between consecutive images Lucas–Kanade algorithm small recurrent convolutional network poses. Combining poses difficult due differences global local coordinate systems. Current graph optimization (PGO) align In this work, propose fusion better integrate predictions, enhancing accuracy estimates. We evaluate eight different units create simulation pre-train APR for improved generalization. Additionally, record large dataset various scenarios challenging indoor resembling transportation robots. Through hyperparameter searches experiments, demonstrate that our method outperforms PGO effectiveness.

Язык: Английский

Процитировано

2
GPS-Induced Disparity Correction for Accurate Object Placement in Augmented Reality DOI Creative Commons
Sungkwan Youm,

Nyum Jung,

Sunghyun Go

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(7), С. 2849 - 2849

Опубликована: Март 28, 2024

The use of augmented reality (AR) continues to increase, particularly in marketing and advertising, where virtual objects are showcased the AR world, thereby expanding its various applications. In this paper, a method linking coordinate systems connect metaverse with real world is proposed system for correcting displaying environment implemented. calculates errors accurately represent presents show these without errors. was verified through experiments successfully display AR. To minimize localization errors, semantic segmentation used recognize estimate buildings, device location. An error correction expression also presented. designed correct AR, confirmed functionality location correction.

Язык: Английский

Процитировано

1
A Comparative Analysis of Traditional Deep Learning Framework for 3D Object Pose Estimation DOI

Davesh Singh Som,

Pawan Kumar Goel,

Deepak Singh Rana

и другие.

Опубликована: Март 15, 2024

3-d object pose estimation is an essential mission for expertise three-D scenes, and it has won sizeable attention in current years, its various applications robotics, augmented reality, autonomous riding. Deep trendy emerged as a powerful approach 3D item ultra-modern capability to automatically research features from raw records capture complicated spatial relationships. On this look, we behavior comparative evaluation of cutting-edge conventional deep contemporary frameworks estimation. We assessment the strategies used their obstacles. Then, discuss idea present day how been implemented venture. compare performance different getting know modern frameworks, together with Convolutional Neural Networks (CNNs), Recurrent (RNNs), Generative adverse (GANs), on benchmark datasets

Язык: Английский

Процитировано

1
Linear target change detection from a single image based on three‐dimensional real scene DOI
Yang Liu, Zheng Ji, Lingfeng Chen

и другие.

The Photogrammetric Record, Год журнала: 2023, Номер 38(184), С. 617 - 635

Опубликована: Дек. 1, 2023

Abstract Change detection is a critical component in the field of remote sensing, with significant implications for resource management and land monitoring. Currently, most conventional methods sensing change often rely on qualitative monitoring, which usually requires data collection from entire scene over multiple time periods. In this paper, we propose method that can be computationally intensive lacks reusability, especially when dealing large datasets. We use novel methodology leverages texture features geometric structure information derived three‐dimensional (3D) real scenes. By establishing two‐dimensional (2D)–3D relationship between single observational image corresponding 3D scene, obtain more accurate positional image. This allows us to transfer depth model image, thereby facilitating precise measurements specific planar targets. Experimental results indicate our approach enables millimetre‐level minuscule targets based Compared methods, technique offers enhanced efficiency making it valuable tool fine‐grained small scene.

Язык: Английский

Процитировано

1
Experimental Investigation on Operating Conditions for Augmented Reality Utilizing Indoor Building Element's Images as Markers for Construction and Facility Management DOI
Bilawal Mahmood, Sang‐Uk Han, Seok Kim

и другие.

Опубликована: Янв. 1, 2024

Augmented reality (AR) is evolving as an onsite visualization tool that can facilitate expedited retrieval and enhanced comprehension of design information in field settings. However, traditional approaches, such marker-based markerless AR, either require additional time effort for marker installation maintenance or face challenges recognizing reference targets stemming from the self-similarity indoor environments. By leveraging benefits offered by both this study investigates suitability using existing building elements with known spatial coordinates a virtual model (e.g., Building Information Model (BIM)) AR implementations. Experiments were conducted to assess performance developed implementation six diverse geometries dimensions across several distinct operational scenarios, including variety distances viewing angles. The experimental results indicated mean errors 0.315 m 4.801° camera-pose estimation, suggesting serve effectively natural markers under optimal working conditions. These conditions include capturing images frontal perspective, at no greater than approximately twice longest dimension target object. findings imply environments have potential be used implementation.

Язык: Английский

Процитировано

0