Registration Of Virtual Objects Research Articles

3D Reconstruction Method of Virtual and Real Fusion Based on Machine Learning

With the continuous development of computer vision technology, people are paying more and more attention to the method of using computers to simulate actual 3D scenes, and the requirements for 3D reconstruction technology are getting higher and higher. Virtual and real fusion refers to combining the virtual environment generated by the computer with the actual scenes around the user through photoelectric display, sensors, computer graphics, multimedia, and other technologies. This is a technology that can obtain more convenient and direct expressions, and it is also a technique for expressing content more abundantly and accurately. The key to virtual and real fusion technology is the registration of virtual objects and real scenes. It means that the system should be able to correctly estimate the position and posture of the camera in the real world, and then place the virtual object where it should be. Machine learning is a multifield interdisciplinary subject that specializes in how computers simulate or realize human learning behaviors. It is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are in all the fields of artificial intelligence. This article introduces the virtual-real fusion 3D reconstruction method based on machine learning, compares the performance of the method with other algorithms through experiments, and draws the following conclusion: the algorithm in this study is the fastest, with an average speed of 72.9% under different times. To evaluate the image acquisition indicators of each algorithm, the algorithm in this study has the lowest error rate. The matching accuracy of each algorithm is tested, and it is found that the average matching accuracy of the algorithm in this study is about 0.87, which is the highest.

3D registration based on V-SLAM and application in augmented reality

Augmented reality is currently a research hotspot in the field of computer vision and computer graphics, and its applications are becoming more and more extensive. One of its key technologies is the three-dimensional registration of virtual objects. Three-dimensional registration requires accurate camera pose estimation and scene three-dimensional reconstruction. Therefore, this paper studies the 3D registration based on visual SLAM, and mainly contributes the following three aspects. (1) A dense matching method based on a depth camera is proposed, which can be well applied to scenes where the camera moves fast or rotates strongly, such as augmented reality. (2) A dense reconstruction method based on Voxel Hashing is designed, which alleviates the low computational efficiency and low precision of the existing RGB-D SLAM method. (3) A simple augmented reality system was designed to verify the effects of the registration and fusion of virtual objects. Experiments show that compared with the state-of-the-art methods, the algorithm proposed in this paper generates a more refined and smooth reconstructed model, and the virtual-real fusion effect based on this model is also more realistic.

Image real-time augmented reality technology based on spatial color and depth consistency

Augmented reality can enhance people’s perception of the environment by embedding virtual objects or other information in real-time images. In this paper, the color image is used as a reference to calculate the confidence of the original depth map, and stereo matching is performed according to the feature points. The depth map is mainly enhanced by the color, edge, and segmentation results of the color image. A deep computing system based on augmented reality is designed. The system can use a binocular camera to collect object images in real time and obtain better parallax images by correcting the calibrated image. The semi-global block matching algorithm and depth calculation are used to realize the tracking registration of virtual objects. Experiments in different environments show that the system has good real-time performance, light invariance, and depth consistency.

A SLAM-Based Mobile Augmented Reality Tracking Registration Algorithm

This paper proposes a simultaneous localization and mapping (SLAM)-based markerless mobile-end tracking registration algorithm to address the problem of virtual image drift caused by fast camera motion in mobile-augmented reality (AR). The proposed algorithm combines the AGAST-FREAK-SLAM algorithm with inertial measurement unit (IMU) data to construct a scene map and localize the camera’s pose. The extracted feature points are matched with feature points in a map library for real-time camera localization and precise registration of virtual objects. Experimental results show that the proposed method can track feature points in real time, accurately construct scene maps, and locate cameras; moreover, it improves upon the tracking and registration robustness of earlier algorithms.

Relative Effects of Real-world and Virtual-World Latency on an Augmented Reality Training Task: An AR Simulation Experiment

In Augmented Reality (AR), virtual objects and information are overlaid onto the user’s view of the physical world and can appear to become part of the real world. Accurate registration of virtual objects is a key requirement for an effective and natural AR system, but misregistration can break the illusion of virtual objects being part of the real world and disrupt immersion. End-to-end system latency severely impacts the quality of AR registration. In this research, we present a controlled study that aims at a deeper understanding of the effects of latency on virtual and real world imagery and its influences on task performance in an AR training task. We utilize an AR Simulation approach, in which an outdoor AR training task is simulated in a high-fidelity VR system. The real and augmented portions of the AR training scenarios are simulated in VR, affording us detailed control over a variety of immersion parameters and the ability to explore the effects of different types of simulated latency. We utilized a representative task inspired by outdoor AR military training systems to compare various AR system configurations, including optical see-through and video see-through setups with both matched and unmatched levels of real and virtual objects latency. Our findings indicate that users are able to perform significantly better when virtual and real-world latencies are matched (as in the case of simulated video-see-through AR with perfect augmentation-to-real-world registration). Unequal levels of latency led to reduction in performance, even when overall latency levels were lower compared to the matched case. The relative results hold up with increased overall latency.

Vision-Based Hand Detection for Registration of Virtual Objects in Augmented Reality

This paper presents a markerless Augmented Reality (AR) framework which utilizes outstretched hand for registration of virtual objects in the real environment. Hand detection methods used in this work are purely based on computer vision algorithms to detect bare hand without assistance from markers or any other devices such as mechanical devices and magnetic devices. We use a stereo camera to capture video images, so that the depth information of the hand can be constructed. Skin color segmentation is employed to segment the hand region from the images. Instead of fiducial markers, the center of the palm and fingertips are tracked in real time. Incorporating the depth information computed, the 3D positions of these hand features are then used to estimate the 6DOF camera pose with respect to the hand, which in turn allows the virtual objects to be augmented onto the palm accurately. This method increases the ease of manipulation of AR objects. Users can inspect and manipulate the AR objects in an intuitive way by using their bare hands.

Registration of a 3D Model on a Single Image in Wide Angle Augmented Reality

This paper presents a new design of a wide angle augmented reality (AR) system and an effective registration method using a single image. The AR system is designed to meet three requirements: (1) the field of view (FOV) of the AR system must be wide enough to view reference constructions in outdoor scenes; (2) the precise registration of virtual objects should be guaranteed at any position of the image; and (3) the registration should be valid after motion of the camera. We use camera calibration and calibration-free methodologies in our approach. The internal parameters of the camera are determined roughly by the camera calibration and how the distortion coefficient of the camera lens is refined. The external parameters are determined by the calibration-free approach. At least four coplanar points forming a projected parallelogram are needed to construct the affine coordinates that are used for the registration. The only presumed condition is that the user specifies some projected parallelogram lines of the reference object interactively. The experimental results demonstrate the display prototype of the wide angle AR system with the distorted images removed and registration of 3D virtual models in static and dynamic camera environments.