A study on recognizing multi-real world object and estimating 3D position in augmented reality

Lee, Taemin; Jung, Changhun; Lee, Kyung-Taek; Seo, Sanghyun

doi:10.1007/s11227-021-04161-0

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…Thus, it will be necessary to be able to make accurate object predictions based on sufficient datasets in various real-world situations. Therefore, more accurate AR registration in the outdoor environment will still require the use of methods based on more sensors (e.g., multiple cameras, depth sensors) and more datasets (e.g., image data, labeling, and point clouds) for deep-learning approaches like research works in the field of computer vision [40]. Moreover, realistic visualization issues for 3D representation of the physical environment related to depth perception between real-world objects and the virtual charter will need to be resolved.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Real-Time Motion Adaptation with Spatial Perception for an Augmented Reality Character

Kim,

Chae,

Kim

et al. 2024

Applied Sciences

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Virtual characters are now widely used in games, computer-generated (CG) movies, virtual reality (VR), and communication media. The continued technological innovations in motion capture mean that a more natural representation of a three-dimensional character’s motion should be achievable. Many researchers have investigated how virtual characters interact with their surrounding environment through spatial relationships, which were introduced for adapting and preserving character motion. However, technical problems should be resolved to enable the control of characters in augmented reality (AR) environments that combine with the real world, and this can be achieved by adapting motion to environmental differences using original motion datasets. In this paper, we investigate a novel method for preserving automatic motion adaptation for a virtual character in AR environments. We used specific object (e.g., puddle) recognition and the spatial properties of the user’s surrounding space, e.g., object types and positions, and ran validation experiments to provide accurate motion to improve the AR experience. Our experimental study showed positive results in terms of smooth motion in AR configurations. We also found that the participants using AR felt a greater sense of co-presence with the character through adapted motion.

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Section: Discussion and Limitationsmentioning

confidence: 99%

Real-Time Motion Adaptation with Spatial Perception for an Augmented Reality Character

Kim,

Chae,

Kim

et al. 2024

Applied Sciences

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“…This technology currently plays a pivotal role in a variety of application scenarios, and its demand is steadily increasing with the development of industrial automation and spatial computing technologies. Currently, in specific application domains, there has been notable research progress, such as in industrial automation [16][17][18], autonomous driving [19][20][21], and virtual and augmented reality [22,23], among others. In the context of industrial automation, object pose estimation serves as a critical tool for assisting robots in determining the position and orientation of objects, enabling more precise grasping, assembly, or docking.…”

Section: Related Work 21 Pose Estimationmentioning

confidence: 99%

DCSPose: A Dual-Channel Siamese Framework for Unseen Textureless Object Pose Estimation

Yue,

Han,

Yang

et al. 2024

Applied Sciences

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The demand for object pose estimation is steadily increasing, and deep learning has propelled the advancement of this field. However, the majority of research endeavors face challenges in their applicability to industrial production. This is primarily due to the high cost of annotating 3D data, which places higher demands on the generalization capabilities of neural network models. Additionally, existing methods struggle to handle the abundance of textureless objects commonly found in industrial settings. Finally, there is a strong demand for real-time processing capabilities in industrial production processes. Therefore, in this study, we introduced a dual-channel Siamese framework to address these challenges in industrial applications. The architecture employs a Siamese structure for template matching, enabling it to learn the matching capability between the templates constructed from high-fidelity simulated data and real-world scenes. This capacity satisfies the requirements for generalization to unseen objects. Building upon this, we utilized two feature extraction channels to separately process RGB and depth information, addressing the limited feature issue associated with textureless objects. Through our experiments, we demonstrated that this architecture effectively estimates the three-dimensional pose of objects, achieving a 6.0% to 10.9% improvement compared to the state-of-the-art methods, while exhibiting robust generalization and real-time processing capabilities.

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“…Their ability to visualize the patient-specific anatomy during affected tissue extraction allows them to work within safe workspace boundaries. While the precise mapping of medical images is unlikely due to the constant deformation of tissue pre-and post-surgery, many research papers [52][53][54][55] are dedicated to exploring the possibility of decoupling virtual objects and their sensory stimuli from the real world using algorithmic approaches adapted from the DL repository. Amongst the most acclaimed methods, projection-based AR, marker-based AR, markerless AR, and superimposition AR are widely used in robotic platforms employed in the operation theater and remotely.…”

Section: Object Detection and Ar Alignment For Robotic Surgerymentioning

confidence: 99%

Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions

Seetohul

Shafiee

Sirlantzis

2023

Sensors

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Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human–robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future.

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A study on recognizing multi-real world object and estimating 3D position in augmented reality

Cited by 6 publications

References 25 publications

Real-Time Motion Adaptation with Spatial Perception for an Augmented Reality Character

Real-Time Motion Adaptation with Spatial Perception for an Augmented Reality Character

DCSPose: A Dual-Channel Siamese Framework for Unseen Textureless Object Pose Estimation

Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions

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