Redirected Walking (RDW) enables continuous locomotion in virtual environments (VEs) within limited physical spaces. However, classic RDW methods rely on static settings with tight boundaries, which can reduce their applicability in large shared physical spaces where boundary constraints are not dominant, especially under dynamic and multi-user conditions. To overcome this, we introduce a new task: Redirected Walking in Open Physical Spaces (OPSRDW), which allows users to navigate expansive VEs safely and naturally despite dynamic obstacles and without requiring a fixed boundary model. We further propose Dynamic Control and Redirection with Safety Constraints (DyCoRe), which formulates OPS-RDW as a constrained optimization problem. DyCoRe uses Dynamic Control Barrier Functions to model real-time collision avoidance constraints and solves an online Quadratic Programming problem to compute optimal velocities that minimize path deviation while ensuring safety. These velocities are mapped into real-time redirection gains, guiding users along natural paths while reducing collision risk. A relaxation mechanism is incorporated to handle infeasible scenarios. Extensive simulations suggest consistent improvements in safety and obstacle clearance over state-of-theart methods. User studies demonstrate that DyCoRe significantly improves navigation continuity, reduces the number of resets, and tends to reduce perceived discomfort compared to classic RDW strategies. DyCoRe provides an efficient and learning-free solution for safe and natural VR locomotion in open physical spaces with dynamic obstacles and multiple users.

Cybersickness is a major challenge in virtual reality (VR), adversely affecting user comfort and usability. While prior research has examined visual and system factors, the impact of different VR tasks under varied rendering conditions remains underexplored. To address this, we investigated three interaction tasks—navigation, selection, and manipulation—together with two rendering parameters: center area radius (CAR) and peripheral resolution (PR). Cybersickness severity and frequency were assessed using Simulator Sickness Questionnaire (SSQ) scores and electroencephalography (EEG) data. Results show that task type significantly affects cybersickness severity, with CAR playing a critical role. Moreover, α-band power spectral density (PSD) strongly correlates with SSQ scores, suggesting its potential as a biomarker for cybersickness. Neural responses also exhibited temporal delays compared to subjective reports, offering insights into the mechanisms of cybersickness. These findings advance understanding of task- and rendering-related influences, informing more effective prediction and mitigation strategies in VR system design.

Intuitive motion control is essential for virtual reality, allowing users to manipulate objects naturally while receiving realistic and responsive visual feedback. 3D Gaussian splatting provides real-time, photorealistic scene rendering, making it promising for virtual reality applications. Still, it falls short in accurate motion control of dynamic objects due to its unstructured global motion representation and redundant motion learning. To address these problems, we propose a motion hierarchical Gaussian based dynamic control method. First, a motion hierarchical Gaussian representation is introduced and initialized with semantic and deformation information. Then a motion hierarchical decomposition method is proposed to optimize the local motion in the representation. The representation is next optimized by a local motion analysis based refinement method. We also design a set of motion control operations for the motion hierarchical Gaussian. Experimental results show that our method achieves high-precision motion reconstruction, accurate motion decomposition, real-time, intuitively and immersive VR motion control.

Transferring existing Human-Object Interaction (HOI) motion to novel objects is essential for robotics, virtual reality. Traditional approaches only model spatial surface correspondences between humans and source objects or between source and target objects, ignoring the internal topological structures of humans, the internal topology of objects, the non-surface spatial topological relationships, and temporal motion relations. In this paper, we propose a spatial-temporal relation guided motion transfer framework. Firstly, we define a spatial-temporal relation interaction graph representation(STRIG) to model the human internal topology, object internal topology and human-object global topology together with the temporal motion relation. We propose a STRIGs-guided motion transfer diffusion model for generating spatially, semantically and temporally consistent HOI motions that are adapted to novel objects. To tackle the absence of ground-truth motions after transfer, we introduce a spatial-temporal relation optimization strategy. Extensive experiments demonstrate that our method consistently outperforms other approaches in terms of motion transfer quality, performance, and sequence stability, with particularly robustness under large variations in target object topology.

Effective Augmented Reality (AR) guidance for complex assembly faces a dual challenge: the inability of conventional liaison graphs to represent procedural logic, and the cognitive burden imposed by visual instructions. We argue that the solution requires a more expressive structure to overcome these representational deficits and a narration approach to mediate instruction complexity. Our method first employs an assembly hypergraph to capture the task's hierarchical information, from which an A* search algorithm generates an optimal assembly path. Then a Large Language Model (LLM)-mediated narration workflow is designed to address the ergonomic deficiencies of the machine-centric path. It employs an optimizer to improve fluency, followed by a narrator that crafts the steps into an intuitive instruction narration. A within-subjects user study (N = 24) revealed a progressive enhancement from our method's components. The transition from a liaison-graph baseline to the hypergraph alone improved objective outcomes by reducing task time and errors and improving subjective ratings (SUS, NASA-TLX, TAM, and ARI). Subsequently, augmenting the LLM-mediated narration maintained these gains while lowering cognitive load and elevating user experience and usability. Our findings indicate the value of our AR assembly design and discuss the opportunities of using LLM as a mediation layer for better user interaction.

3D multi-object arrangement is a fundamental task in VR that relies on accurate and natural initial selection alongside rapid and convenient subsequent manipulation to ensure high efficiency. However, existing methods fail to support efficient multi-object arrangement in highly occluded scenes with densely packed candidate objects through controller-free natural interactions. In this article, we propose an efficient, scene-aware multi-object arrangement method (MOA) designed for fast, precise, and convenient object arrangement. First, MOA introduces an importance-driven multi-object initial selection algorithm that assigns higher spatiotemporally correlated object importance (IMP) to target objects, establishing a natural multi-object initial selection mode that enables quick and accurate selection of high-IMP objects. Subsequently, it presents an auxiliary-structure-guided multi-object manipulation algorithm that constructs an auxiliary manipulation structure to assist subsequent multi-object manipulation, alongside a multi-modal interaction mode that facilitates swift and natural manipulation. Compared to state-of-the-art controller-free and controller-based methods, MOA significantly improves task performance, reduces task load, and enhances convenience in complex multi-object arrangement scenes involving hundreds of highly occluded objects need to be arranged.

Group navigation is a virtual reality (VR) technology designed to replace personal navigation, enhancing efficiency and aiding guided tours in path planning for other users. Group navigation techniques require users to have a clear understanding of navigation and travel modalities. Tour guides must ensure that users do not intersect with the environment or other users' models and must provide reasonable tour formations. To address these needs, we propose a scene-aware automatic formation generation method for fast and easy-to-use guided tours. First, we generate a limited number of candidate points for the exhibition and initialize the visiting group formation. Next, we optimize the formation to maximize view quality using our proposed viewpoint observation score. Finally, we match the visitors to the optimized formation to ensure a minimal view deflection angle. Moreover, we conducted a user study to evaluate the performance of our approach. Compared to the current method, our approach significantly increased navigation efficiency and view quality. Additionally, it substantially decreased the task load and improved the system usability for both tour guides and visitors.

Object grouping task is an important research direction for fast manipulation of a large number of objects. It can help users to improve the efficiency of multi-object manipulation. However, the current research on this aspect is still immature. For this task, in this paper, based on the brush metaphor, we propose a method for grouping objects based on bare hands in virtual reality scenes. We design a number of interactions to facilitate the user's grouping of objects in the three-dimensional virtual space. Object grouping in virtual reality could encompass two subtasks: group generation and group modification. The emphasis of these tasks varies, with the former focusing on creating groups from ungrouped objects and the latter focusing on modifying group members once they are generated. The results of the empirical study show that our method has better performance in accomplishing both sub-tasks compared to the Ray method, Screen method and Cone method.

This article describes a mixed reality visual instruction approach for remote collaboration between a trainee and an expert. The expert authors the visual instructions through a virtual reality interface. The instructions are shown to the trainee overlaid onto the workspace using an augmented reality interface. The approach achieves effectiveness and efficiency by addressing three challenges. First, the expert-authored visual instructions are shown to the trainee by taking into account occlusions with the 3D workspace; Second, in addition to abstract visual instructions implemented by arrows, the expert can also author highly suggestive instructions by depicting the target state of the workspace realistically by selecting, copying, pasting, and repositioning workspace objects; Third, multiple instructions can be concatenated in sequences that the trainee executes on their own, without any additional guidance from the expert; The approach has been evaluated in a controlled user study with three experiments. The experiment verification confirms that compared to the conventional instruction, this approach achieves significantly lower error rates, shorter task completion times, and lower rotation angular errors. Moreover, the approach allows the trainee to execute the entire sequence robustly, without real-time instruction from the expert.

Rendering quality and performance greatly affect the user's immersion in VR experiences. 3D Gaussian Splatting-based methods can achieve photo-realistic rendering with speeds of over 100 fps in static scenes, but the speed drops below 10 fps in monocular dynamic scenes. Foveated rendering provides a possible solution to accelerate rendering without compromising visual perceptual quality. However, 3DGS and foveated rendering are not compatible. In this paper, we propose Fov-GS, a foveated 3D Gaussian splatting method for rendering dynamic scenes in real time. We introduce a 3D Gaussian forest representation that represents the scene as a forest. To construct the 3D Gaussian forest, we propose a 3D Gaussian forest initialization method based on dynamic-static separation. Subsequently, we propose a 3D Gaussian forest optimization method based on deformation field and Gaussian decomposition to optimize the forest and deformation field. To achieve real-time dynamic scene rendering, we present a 3D Gaussian forest rendering method based on HVS models. Experiments demonstrate that our method not only achieves higher rendering quality in the foveal and salient regions compared to the SOTA methods but also dramatically improves rendering performance, achieving up to 11.33X speedup. We also conducted a user study, and the results prove that the perceptual quality of our method has a high visual similarity with the ground truth.

In virtual reality applications, in addition to visual feedback, real objects can be used as props for virtual objects to provide passive haptic feedback, which greatly enhances user immersion. Usually, real object props are not one-to-one correspondence with virtual objects. Haptic retargeting technique is proposed to establish the virtual-real correspondence by introducing an offset between the virtual hand and the real hand. Sometimes, the offset is too large to cause user discomfort, and it is necessary to introduce a reset between two haptic retargeting operations to force the virtual hand and the real hand to coincide in order to eliminate the offset. However, too many resets can interfere with this immersion. To address this problem, we propose a haptic retargeting method based on proxy importance calculation using multiple props in virtual reality. The concept of proxy importance for props is introduced first, and then a proxy importance based prop selection and placement method for moving virtual objects are proposed. We also improve the performance of our method by using the props' weighted proxy importance strategy for multi-user collaboration. Compared to the state-of-the-art methods, our method significantly reduces the number of resets, the task completion time, hand movement distances, and task load without the cost of cybersickness in the single-user task. In the multi-user collaborative task, our method also achieves significant improvement using the strategy that weights the proxy importance of the props.

3D scene graph generation is essential for spatial computing in Extended Reality (XR), providing structured semantics for task planning and intelligent perception. However, unlike instance-segmentation-driven setups, generating semantic scene graphs still suffer from limited accuracy due to coarse and noisy point cloud data typically acquired in practice, and from the lack of interactive strategies to incorporate users' spatialized and intuitive guidance. We identify three key challenges: designing controllable interaction forms, involving guidance in inference, and generalizing from local corrections. To address these, we propose SGSG, a Stroke-Guided Scene Graph generation method that enables users to interactively refine 3D semantic relationships and improve predictions in real time. We propose three types of strokes and a lightweight SGstrokes dataset tailored for this modality. Our model integrates stroke guidance representation and injection for spatio-temporal feature learning and reasoning correction, along with intervention losses that combine consistency-repulsive and geometry-sensitive constraints to enhance accuracy and generalization. Experiments and the user study show that SGSG outperforms state-of-the-art methods 3DSSG and SGFN in overall accuracy and precision, surpasses JointSSG in predicate-level metrics, and reduces task load across all control conditions, establishing SGSG as a new benchmark for interactive 3D scene graph generation and semantic understanding in XR. Implementation resources are available at: https://github.com/Sycamore-Ma/SGSG-runtime.

With the increasing complexity of geometry and rendering effects in virtual reality (VR) scenes, existing foveated rendering methods for VR head-mounted displays (HMDs) struggle to meet users' demands for VR scene rendering with high frame rates ($\geq 60fps$≥60fps for rendering binocular foveated images in VR scenes containing over 50 m triangles). Current research validates that auditory content affects the perception of the human visual system (HVS). However, existing foveated rendering methods primarily model the HVS's eccentricity-dependent visual perception ability on the visual content in VR while ignoring the impact of auditory content on the HVS's visual perception. In this article, we introduce an auditory-content-based perceived rendering quality analysis to quantify the impact of visual perception under different auditory conditions in foveated rendering. Based on the analysis results, we propose an audio-visual aware foveated rendering method (AvFR). AvFR first constructs an audio-visual feature-driven perception model that predicts the eccentricity-based visual perception in real time by combining the scene's audio-visual content, and then proposes a foveated rendering cost optimization algorithm to adaptively control the shading rate of different regions with the guidance of the perception model. In complex scenes with visual and auditory content containing over 1.17 m triangles, AvFR renders high-quality binocular foveated images at an average frame rate of 116$fps$fps. The results of the main user study and performance evaluation validate that AvFR achieves significant performance improvement (up to 1.4× speedup) without lowering the perceived visual quality compared with the state-of-the-art VR-HMD foveated rendering method.

Foveated rendering provides an idea for improving the image synthesis performance of neural radiance fields (NeRF) methods. In this article, we propose a scene-aware foveated neural radiance fields method to synthesize high-quality foveated images in complex VR scenes at high frame rates. First, we construct a multi-ellipsoidal neural representation to enhance the neural radiance field's representation capability in salient regions of complex VR scenes based on the scene content. Then, we introduce a uniform sampling based foveated neural radiance field framework to improve the foveated image synthesis performance with one-pass color inference, and improve the synthesis quality by leveraging the foveated scene-aware objective function. Our method synthesizes high-quality binocular foveated images at the average frame rate of 66 frames per second ($FPS$FPS) in complex scenes with high occlusion, intricate textures, and sophisticated geometries. Compared with the state-of-the-art foveated NeRF method, our method achieves significantly higher synthesis quality in both the foveal and peripheral regions with 1.41-1.46× speedup. We also conduct a user study to prove that the perceived quality of our method has a high visual similarity with the ground truth.

Passive haptics utilize the shape of a physical object to convey feedback to the user and enhance immersion in virtual reality. Haptic retargeting is a passive haptic interaction method. Its mapping of physical objects to virtual objects solves the matching problem between virtual and physical objects in the passive haptic method. However, most existing haptic retargeting methods improve efficiency without considering the important factor of user comfort. In this article, we propose an efficient and comfortable haptic retargeting method based on reset point optimization. First, we construct two maps indicating user interaction comfort: the RULA score map and the dominant hand gain map. Subsequently, we propose a reset point optimization algorithm based on these two maps. Moreover, we also optimize the selection of the physical proxy and the placement location when the reset occurs. The user study results show a significant improvement in the efficiency and comfort of our method compared to state-of-the-art methods.

In extended reality (XR) applications, enhancing user perception often necessitates head-mounted display (HMD) removal. However, existing methods suffer from low time performance and suboptimal reconstruction quality. In this paper, we propose a half face mapping 3D Gaussian splatting avatar based HMD removal method (HFM-GS), which can perform real-time and high-fidelity online restoration of the complete face in HMD-occluded videos for XR applications after a short un-occluded face registration. We establish a mapping field between the upper and lower face Gaussians to enhance the adaptability to deformation. Then, we introduce correlation weight-based sampling to improve time performance and handle variations in the number of Gaussians. At last, we ensure model robustness through Gaussian Segregation Strategy. Compared to two state-of-the-art methods, our method achieves better quality and time performance. The results of the user study show that fidelity is significantly improved with our method.

Group selection in virtual reality is an important means of multi-object selection, which allows users to quickly group multiple objects and can significantly improve the operation efficiency of multiple types of objects. In this paper, we propose a group selection method based on multiple rounds of probability permutation, in which the efficiency of group selection is substantially improved by making the object layout of the next round easier to be batch-selected through interactive selection, object grouping probability computation, and position rearrangement in each round of the selection process. We conducted ablation experiments to determine the algorithm coefficients and validate the effectiveness of the algorithm. In addition, an empirical user study was conducted to evaluate the ability of our method to significantly improve the efficiency of the group selection task in an immersive virtual reality environment. The reduced operations also indirectly reduce the user task load and improve usability.

The demand for fluid simulation and rendering in virtual reality (VR) is increasing. However, achieving high visual quality while maintaining real-time efficiency remains a challenge. Traditional foveated rendering methods balance the simulation quality in the foveated region but neglect the physical realism in the peripheral areas, and fail to account for the perceptual degradation caused by frame rate fluctuations during adaptive updates. To address these challenges, we propose a novel visual perception driven fluid rendering method ViP-Fluid, which further enhances rendering quality while balancing efficiency. Our approach employs a spatiotemporal saliency model for multi-granularity simulation and rendering of Lagrangian fluid systems, and introduces a Perception Threshold for Physical Process Elapsing (PTPE) metric, which guides our temporal acceleration strategy. Through a series of objective experiments, we demonstrate the advantages of our method in rendering quality and performance efficiency. ViP-Fluid demonstrates superior metrics not only in the foveated region but also in the salient and overall regions, achieving up to 2.15 times speed-up compared to the high-resolution Position Based Fluids (PBF) benchmark. Subsequent user experiments further validate the visual perception advantages of ViP-Fluid over both traditional and state-of-the-art methods, confirming the spatiotemporal fidelity of our acceleration strategy as well as a user preference for our approach.

Text entry poses a significant challenge in the realm of virtual reality (VR). This paper introduces FanPad, a novel solution designed to facilitate dual-hand text input within head-mounted displays (HMDs). FanPad accomplishes this by ingeniously mapping and curving the 26 typing keys (T26) QWERTY keyboard onto the touchpads of both controllers. The curved key layout of FanPad is derived from the natural movement of the thumb when interacting with the touchpad, resembling an arc with a thumb-length fixed radius.To optimize the experience, we introduce a customization process for the FanPad curve to better cope with individual hand shapes and thumb movements. We also provide a version with more overlap area named FanPad-Ov for different users with different typing habits.Our first user study examined the effects of curving and different overlap areas by comparing four potential layouts. The results clearly favor the FanPad and FanPad-Ov layout compared to the nocurving version, SKPad(-Ov). Subsequently, the second user study was conducted to assess long-term performance and improvement on customized FanPads. Notably, novices achieved a typing speed of 19.73 words per minute (WPM), demonstrating a remarkable increase of 58.47% after a 60-phrase training in six days. The highest typing speed reached an impressive 24.19 WPM.

Neural radiance fields (NeRF) has achieved revolutionary breakthrough in the novel view synthesis task for complex 3D scenes. However, this new paradigm struggles to meet the requirements for real-time rendering and high perceptual quality in virtual reality. In this paper, we propose VPRF, a novel visual perceptual based radiance fields representation method, which for the first time integrates the visual acuity and contrast sensitivity models of human visual system (HVS) into the radiance field rendering framework. Initially, we encode both the appearance and visual sensitivity information of the scene into our radiance field representation. Then, we propose a visual perceptual sampling strategy, allocating computational resources according to the HVS sensitivity of different regions. Finally, we propose a sampling weight-constrained training scheme to ensure the effectiveness of our sampling strategy and improve the representation of the radiance field based on the scene content. Experimental results demonstrate that our method renders more efficiently, with higher PSNR and SSIM in the foveal and salient regions compared to the state-of-the-art FoV-NeRF. The results of the user study confirm that our rendering results exhibit high-fidelity visual perception.

Optimizing rendering performance improves the user's immersion in virtual scene exploration. Foveated rendering uses the features of the human visual system (HVS) to improve rendering performance without sacrificing perceptual visual quality. We collect and analyze the viewing motion of different locomotion methods, and describe the effects of these viewing motions on HVS's sensitivity, as well as the advantages of these effects that may bring to foveated rendering. Then we propose the locomotion-aware foveated rendering method (LaFR) to further accelerate foveated rendering by leveraging the advantages. In LaFR, we first introduce the framework of LaFR. Secondly, we propose an eccentricity-based shading rate controller that provides the shading rate control of the given region in foveated rendering. Thirdly, we propose a locomotion-aware log-polar mapping method, which controls the foveal average shading rate, the peripheral shading rate decrease speed, and the overall shading quantity with the locomotion-aware coefficients based on the eccentricity-based shading rate controller. LaFR achieves similar perceptual visual quality as the conventional foveated rendering while achieving up to 1.6× speedup. Compared with the full resolution rendering, LaFR achieves up to 3.8× speedup.

When searching for a dynamic target in an unknown real world scene, search efficiency is greatly reduced if users lack information about the spatial structure of the scene. Most target search studies, especially in robotics, focus on determining either the shortest path when the target’s position is known, or a strategy to find the target as quickly as possible when the target’s position is unknown. However, the target’s position is often known intermittently in the real world, e.g., in the case of using surveillance cameras. Our goal is to help user find a dynamic target efficiently in the real world when the target’s position is intermittently known. In order to achieve this purpose, we have designed an AR guidance assistance system to provide optimal current directional guidance to users, based on searching a prediction graph. We assume that a certain number of depth cameras are fixed in a real scene to obtain dynamic target’s position. The system automatically analyzes all possible meetings between the user and the target, and generates optimal directional guidance to help the user catch up with the target. A user study was used to evaluate our method, and its results showed that compared to free search and a top-view method, our method significantly improves target search efficiency.

Foveated rendering provides an idea for accelerating rendering algorithms without sacrificing the perceived rendering quality in virtual reality applications. In this paper, we propose a foveated stochastic lightcuts method to render high-quality many-lights illumination effects in high perception-sensitive regions. First, we introduce a spatiotemporal-luminance based lightcuts generation method to generate lightcuts with different accuracy for different visual perception-sensitive regions. Then we propose a multi-resolution light samples selection method to select the light sample for each node in the lightcuts more efficiently. Our method supports full-dynamic scenes containing over 250k dynamic light sources and dynamic diffuse/specular/glossy objects. It provides frame rates up to 110fps for high-quality many-lights illumination effects in high perception-sensitive regions of the HVS in VR HMDs. Compared with the state-of-the-art stochastic lightcuts method using the same rendering time, our method achieves smaller mean squared errors in the fovea and periphery. We also conduct user studies to prove that the perceived quality of our method has a high visual similarity with the results of the ground truth rendered by using the stochastic lightcuts with 2048 light samples per pixel.

This article presents an occlusion management approach that handles fine-grain occlusions, and that quantifies and localizes occlusions as a user explores a virtual environment (VE). Fine-grain occlusions are handled by finding the VE region where they occur, and by constructing a multiperspective visualization that lets the user explore the region from the current location, with intuitive head motions, without first having to walk to the region. VE geometry close to the user is rendered conventionally, from the user's viewpoint, to anchor the user, avoiding disorientation and simulator sickness. Given a viewpoint, residual occlusions are quantified and localized as VE voxels that cannot be seen from the given viewpoint but that can be seen from nearby viewpoints. This residual occlusion quantification and localization helps the user ascertain that a VE region has been explored exhaustively. The occlusion management approach was tested in three controlled studies, which confirmed the exploration efficiency benefit of the approach, and in perceptual experiments, which confirmed that exploration efficiency does not come at the cost of reducing spatial awareness and sense of presence, or of increasing simulator sickness.

VR applications rely on the user's ability to explore the virtual scene efficiently. In complex scenes, occlusions limit what the user can see from a given location, and the user has to navigate the viewpoint around occluders to gain line of sight to the hidden parts of the scene. When the disoccluded regions prove to be of no interest, the user has to retrace their path, making scene exploration inefficient. Furthermore, the user might not be able to assume a viewpoint that would reveal the occluded regions due to physical limitations, such as obstacles in the real world hosting the VR application, viewpoints beyond the tracked area, or viewpoints above the user's head that cannot be reached by walking. Several occlusion management methods have been proposed in visualization research, such as top view, X-ray, and multiperspective visualization, which help the user see more from the current position, having the potential to improve the exploration efficiency of complex scenes. This paper reports on a study that investigates the potential of these three occlusion management methods in the context of VR applications, compared to conventional navigation. Participants were required to explore two virtual scenes to purchase five items in a virtual Supermarket, and to find three people in a virtual parking garage. The task performance metrics were task completion time, total distance traveled, and total head rotation. The study also measured user spatial awareness, depth perception, and simulator sickness. The results indicate that users benefit from top view visualization which helps them learn the scene layout and helps them understand their position within the scene, but the top view does not let the user find targets easily due to occlusions in the vertical direction, and due to the small image footprint of the targets. The X-ray visualization method worked better in the garage scene, a scene with a few big occluders and a low occlusion depth complexity' and less well in the Supermarket scene, a scene with many small occluders that create high occlusion depth complexity. The multi-perspective visualization method achieves better performance than the top view method and the X-ray method, in both scenes. There are no significant differences between the three methods and the conventional method in terms of spatial awareness, depth perception, and simulator sickness.

Virtual Reality (VR) applications allow a user to explore a scene intuitively through a tracked head-mounted display (HMD). However, in complex scenes, occlusions make scene exploration inefficient, as the user has to navigate around occluders to gain line of sight to potential regions of interest. When a scene region proves to be of no interest, the user has to retrace their path, and such a sequential scene exploration implies significant amounts of wasted navigation. Furthermore, as the virtual world is typically much larger than the tracked physical space hosting the VR application, the intuitive one-to-one mapping between the virtual and real space has to be temporarily suspended for the user to teleport or redirect in order to conform to the physical space constraints. In this paper we introduce a method for improving VR exploration efficiency by automatically constructing a multiperspective visualization that removes occlusions. For each frame, the scene is first rendered conventionally, the z-buffer is analyzed to detect horizontal and vertical depth discontinuities, the discontinuities are used to define disocclusion portals which are 3D scene rectangles for routing rays around occluders, and the disocclusion portals are used to render a multiperpsective image that alleviates occlusions. The user controls the multiperspective disocclusion effect, deploying and retracting it with small head translations. We have quantified the VR exploration efficiency brought by our occlusion removal method in a study where participants searched for a stationary target, and chased a dynamic target. Our method showed an advantage over conventional VR exploration in terms of reducing the navigation distance, the view direction rotation, the number of redirections, and the task completion time. These advantages did not come at the cost of a reduction in depth perception or situational awareness, or of an increase in simulator sickness.