Explore the vast range of titles available.

As VR technology advances and network speeds rise, social VR platforms are gaining traction. These platforms enable multiple users to socialize and collaborate within a shared virtual environment using avatars. Virtual reality, with its ability to augment visual information, offers distinct advantages for collaboration over traditional methods. Prior research has shown that merely sharing another person’s viewpoint can significantly boost collaborative efficiency. This paper presents an innovative non-verbal communication technique designed to enhance the sharing of visual information. By employing virtual cameras, our method captures where participants are focusing and what they are interacting with, then displays these data above their avatars. The direction of the virtual camera is automatically controlled by considering the user’s gaze direction, the position of the object the user is interacting with, and the positions of other objects around that object. The automatic adjustment of these virtual cameras and the display of captured images are symmetrically conducted for all participants engaged in the virtual environment. This approach is especially beneficial in collaborative settings, where multiple users work together on a shared structure of multiple objects. We validated the effectiveness of our proposed technique through an experiment with 20 participants tasked with collaboratively building structures using block assembly.

Virtual trackball techniques have become a standard in 3D applications, particularly for interfaces with limited degrees of freedom such as touchscreens or mice. The fact that we are used to them does not mean that they cannot be improved upon. Recent research has highlighted the significance of considering users’ mental models of a preferred rotation axis, as it can improve performance, perceived usability and perceived workload. Building upon these findings, this paper introduces the spheroidal trackball framework—a novel method for orbiting the virtual camera around elongated objects. The paper presents the mathematical formulation and the evaluation of the technique. The formulation offers enough information to implement the approach. The evaluation shows the advantages of this approach over the fixed spherical trackball for this class of objects, in terms of task performance, usability and perceived workload. This research constitutes an advancement in the refinement of 3D user interaction techniques, opening new avenues of innovation in this still evolving field.

Semantic augmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionised image segmentation and classification, its impact on point cloud is an active research field. In this paper, we propose an instance segmentation and augmentation of 3D point clouds using deep learning architectures. We show the potential of an indirect approach using 2D images and a Mask R-CNN (Region-Based Convolution Neural Network). Our method consists of four core steps. We first project the point cloud onto panoramic 2D images using three types of projections: spherical, cylindrical, and cubic. Next, we homogenise the resulting images to correct the artefacts and the empty pixels to be comparable to images available in common training libraries. These images are then used as input to the Mask R-CNN neural network, designed for 2D instance segmentation. Finally, the obtained predictions are reprojected to the point cloud to obtain the segmentation results. We link the results to a context-aware neural network to augment the semantics. Several tests were performed on different datasets to test the adequacy of the method and its potential for generalisation. The developed algorithm uses only the attributes X, Y, Z, and a projection centre (virtual camera) position as inputs.

The construction environment is a highly dynamic and complex system, presenting challenges for accurately identifying and managing dynamic resources in digital twin-based scenes. This study aims to address the problem of object coordinate distortion caused by camera image deformation, which often reduces the fidelity of dynamic object mapping in digital construction monitoring. A novel dynamic object mapping generation method is proposed to enhance precision and synchronization of dynamic objects within a digital twin environment. The approach integrates internal and external camera parameters, including spatial position, field of view (FOV), and camera pose, into BIM using Dynamo, thereby creating a virtual camera aligned with the physical one. The YOLOv11 algorithm is employed to recognize dynamic objects in real-time camera footage, and corresponding object families are generated in the BIM model. Using perspective projection combined with a linear regression model, the system computes and updates accurate coordinate positions of the dynamic objects, which are then fed back into the camera view to achieve real-time mapping. Experimental validation demonstrates that the proposed method significantly reduces mapping errors induced by lens distortion and provides accurate spatial data, supporting improved dynamic resource perception and intelligent management in digital twin construction environments.

We explore multi-log grasping using reinforcement learning and virtual visual servoing for automated forwarding in a simulated environment. Automation of forest processes is a major challenge, and many techniques regarding robot control pose different challenges due to the unstructured and harsh outdoor environment. Grasping multiple logs involves various problems of dynamics and path planning, where understanding the interaction between the grapple, logs, terrain, and obstacles requires visual information. To address these challenges, we separate image segmentation from crane control and utilise a virtual camera to provide an image stream from reconstructed 3D data. We use Cartesian control to simplify domain transfer to real-world applications. Because log piles are static, visual servoing using a 3D reconstruction of the pile and its surroundings is equivalent to using real camera data until the point of grasping. This relaxes the limits on computational resources and time for the challenge of image segmentation, and allows for data collection in situations where the log piles are not occluded. The disadvantage is the lack of information during grasping. We demonstrate that this problem is manageable and present an agent that is 95% successful in picking one or several logs from challenging piles of 2–5 logs.

In this paper we propose a dynamic image-based visual servoing (IBVS) control for a rotary wing unmanned aerial vehicle (UAV) which directly accounts for the vehicle's underactuated dynamic model. The motion control objective is to follow parallel lines and is motivated by power line inspection tasks where the UAV's relative position and orientation to the lines are controlled. The design is based on a virtual camera whose motion follows the onboard physical camera but which is constrained to point downwards independent of the vehicle's roll and pitch angles. A set of image features is proposed for the lines projected into the virtual camera frame. These features are chosen to simplify the interaction matrix which in turn leads to a simpler IBVS control design which is globally asymptotically stable. The proposed scheme is adaptive and therefore does not require depth estimation. Simulation results are presented to illustrate the performance of the proposed control and its robustness to calibration parameter error.

This paper proposes a new method for rectification of single-lens stereovision system with a triprism. The image plane of this camera will capture three different views of the same scene behind the filter in one shot. These three sub-images can be taken as the images captured by three virtual cameras which are generated by the three Face (3F) filter (triprism). A geometry-based method is proposed to determine the pose of virtual cameras and obtain the rotational and translational transformation matrix to real camera. At the same time, the parallelogram rule and refraction rule are employed to determine the desired sketch ray functions. Followed by this, the rectification transformation matrix which applied on the images captured using the system is computed. The approach based on geometry analysis of ray sketching is significantly a simpler implementation: it does not require the usual complicated calibration process. Experimental results are presented to show the effectiveness of the approach.

Virtual 3D city models serve as integration platforms for complex geospatial and georeferenced information and as medium for effective communication of spatial information. In order to explore these information spaces, navigation techniques for controlling the virtual camera are required to facilitate wayfinding and movement. However, navigation is not a trivial task and many available navigation techniques do not support users effectively and efficiently with their respective skills and tasks. In this article, we present an assisting, constrained navigation technique for multiscale virtual 3D city models that is based on three basic principles: users point to navigate, users are lead by suggestions, and the exploitation of semantic, multiscale, hierarchical structurings of city models. The technique particularly supports users with low navigation and virtual camera control skills but is also valuable for experienced users. It supports exploration, search, inspection, and presentation tasks, is easy to learn and use, supports orientation, is efficient, and yields effective view properties. In particular, the technique is suitable for interactive kiosks and mobile devices with a touch display and low computing resources and for use in mobile situations where users only have restricted resources for operating the application. We demonstrate the validity of the proposed navigation technique by presenting an implementation and evaluation results. The implementation is based on service-oriented architectures, standards, and image-based representations and allows exploring massive virtual 3D city models particularly on mobile devices with limited computing resources. Results of a user study comparing the proposed navigation technique with standard techniques suggest that the proposed technique provides the targeted properties, and that it is more advantageous to novice than to expert users.

Research in virtual camera control has focused primarily on finding methods to allow designers to place cameras effectively and efficiently in dynamic and unpredictable environments, and to generate complex and dynamic plans for cinematography in virtual environments. In this article, we propose a novel approach to virtual camera control, which builds upon camera control and player modelling to provide the user with an adaptive point-of-view. To achieve this goal, we propose a methodology to model the player’s preferences on virtual camera movements and we employ the resulting models to tailor the viewpoint movements to the player type and her game-play style. Ultimately, the methodology is applied to a 3D platform game and is evaluated through a controlled experiment; the results suggest that the resulting adaptive cinematographic experience is favoured by some player types and it can generate a positive impact on the game performance.

In this paper, we investigate absolute and relative pose estimation under refraction, which are essential problems for refractive structure from motion. To cope with refraction effects, we first formulate geometric constraints for establishing iterative algorithms to optimize absolute and relative pose. By classifying two scenarios according to the geometric relationship between the camera and refractive interface, we derive the corresponding solutions to solve the optimization problems efficiently. In the scenario where the geometry between the camera and refractive interface is fixed (e.g., underwater imaging), we also show that the refractive epipolar constraint for relative pose can be established as a summation of the classical essential matrix and two correction terms caused by refraction by using the virtual camera transformation. Thanks to its succinct form, the resulting refractive epipolar constraint can be efficiently optimized. We evaluate our proposed algorithms on synthetic data showing superior accuracy and computational efficiency compared to state-of-the-art (SOTA) methods. We further demonstrate the application of the proposed algorithms in refractive structure from motion on real data. Our datasets (Hu et al., RefractiveSfM, https://github.com/diku-dk/RefractiveSfM, 2022) and code (Hu et al., DIKU Refractive Scenes Dataset 2022, Data, 2022) are publicly available.