Procedurally generated virtual reality from 3D reconstructed physical space

Sra, Misha; Garrido-Jurado, Sergio; Schmandt, Chris; Maes, Pattie

doi:10.1145/2993369.2993372

Cited by 107 publications

(39 citation statements)

References 26 publications

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“…Redirection techniques relying on environmental manipulation do, as the name implies, accomplish the redirection by changing the properties of the virtual environment. In its simplest form, this type of redirection involves creation of virtual environments that match the physical space in size and in terms of potential obstacles (Simeone et al 2015;Sra et al 2016). Presenting virtual objects at the boundaries of the tracking space should be able to contain the movement of most walkers.…”

Section: Redirected Walkingmentioning

confidence: 99%

Natural Walking in Virtual Reality

et al. 2018

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Recent technological developments have finally brought virtual reality (VR) out of the laboratory and into the hands of developers and consumers. However, a number of challenges remain. Virtual travel is one of the most common and universal tasks performed inside virtual environments, yet enabling users to navigate virtual environments is not a trivial challenge—especially if the user is walking. In this article, we initially provide an overview of the numerous virtual travel techniques that have been proposed prior to the commercialization of VR. Then we turn to the mode of travel that is the most difficult to facilitate, that is, walking. The challenge of providing users with natural walking experiences in VR can be divided into two separate, albeit related, challenges: (1) enabling unconstrained walking in virtual worlds that are larger than the tracked physical space and (2) providing users with appropriate multisensory stimuli in response to their interaction with the virtual environment. In regard to the first challenge, we present walking techniques falling into three general categories: repositioning systems, locomotion based on proxy gestures, and redirected walking. With respect to multimodal stimuli, we focus on how to provide three types of information: external sensory information (visual, auditory, and cutaneous), internal sensory information (vestibular and kinesthetic/proprioceptive), and efferent information. Finally, we discuss how the different categories of walking techniques compare and discuss the challenges still facing the research community.

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Section: Redirected Walkingmentioning

confidence: 99%

Natural Walking in Virtual Reality

et al. 2018

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“…To overcome these issues, past research on "Substitutional Reality" (SR) investigated the idea of matching physical objects with virtual ones under the assumption that an explicit mismatch exists in the pairing of objects [16] or surfaces [15]. Other research has explored the automatic reconstruction of the physical environment [20] using a set of rules based on the principles of SR. Reality Skins by Shapira and Freedman [14] uses a blueprint, containing the set of objects and materials to use, provided by the designers to assist the substitution process.…”

Section: Related Workmentioning

confidence: 99%

“…Related research has shown that substitutional environments can be automatically generated by processing depth camera input [14,20]. In this research we focused on exploring how best to support users in this alternative approach, which delegates the customisation of the VE to the end-user as a way of addressing the potential of repetitiveness of procedural content generation [18].…”

Section: Introductionmentioning

confidence: 99%

Inside looking out or outside looking in?

Garcia

Simeone

Higgins

et al. 2018

Proceedings of the 2018 International Conference on Advanced Visual Interfaces

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Current Virtual Reality systems only allow users to draw a rectangular perimeter to mark the room-scale area they intend to use. Domestic environments can include furniture and other obstacles that hinder the ease with which users can naturally walk. By leveraging the benefits of passive haptics, users can match physical objects with virtual counterparts, to create substitutional environments. In this paper we explore two visualisation modalities to aid in the creation of a coarse virtual representation of the physical environment, by marking out the volumes of space where physical obstacles are located, to support the substitution process. Our study investigates whether this process is better supported by an inside-looking-out 3D User Interface (that is, viewing the outside world while immersed in Virtual Reality) or from an outside-looking-in one (while viewing the Virtual Environment through an external device, such as a tablet). Results show that the immersive option resulted in better accuracy and was the one with the highest overall preference ratings. CCS CONCEPTS • Human-centered computing → Virtual reality; Interaction techniques;

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“…Augmentation of reality requires the followings: (i) a region to augment must be detected in the image (object detection) and (ii) the rotation for the augmented content must be estimated (pose estimation) [45]. Virtual Reality (VR) transports users into a number of real-world and imagined environments [46], [49], [50], [52], and Mixed Reality (MR) [47], [48], [51] merges both AR and VR technologies. Despite slight differences, their success lies on accurate detection and pose estimation of objects.…”

Section: Introductionmentioning

confidence: 99%

A review on object pose recovery: From 3D bounding box detectors to full 6D pose estimators

Şahin

Garcia-Hernando

Sock

et al. 2020

Image and Vision Computing

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Object pose recovery has gained increasing attention in the computer vision field as it has become an important problem in rapidly evolving technological areas related to autonomous driving, robotics, and augmented reality. Existing review-related studies have addressed the problem at visual level in 2D, going through the methods which produce 2D bounding boxes of objects of interest in RGB images. The 2D search space is enlarged either using the geometry information available in the 3D space along with RGB (Mono/Stereo) images, or utilizing depth data from LIDAR sensors and/or RGB-D cameras. 3D bounding box detectors, producing category-level amodal 3D bounding boxes, are evaluated on gravity aligned images, while full 6D object pose estimators are mostly tested at instance-level on the images where the alignment constraint is removed. Recently, 6D object pose estimation is tackled at the level of categories. In this paper, we present the first comprehensive and most recent review of the methods on object pose recovery, from 3D bounding box detectors to full 6D pose estimators. The methods mathematically model the problem as a classification, regression, classification & regression, template matching, and point-pair feature matching task. Based on this, a mathematical-model-based categorization of the methods is established. Datasets used for evaluating the methods are investigated with respect to the challenges, and evaluation metrics are studied. Quantitative results of experiments in the literature are analysed to show which category of methods best performs across what types of challenges. The analyses are further extended comparing two methods, which are our own implementations, so that the outcomes from the public results are further solidified. Current position of the field is summarized regarding object pose recovery, and possible research directions are identified.

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Procedurally generated virtual reality from 3D reconstructed physical space

Cited by 107 publications

References 26 publications

Natural Walking in Virtual Reality

Natural Walking in Virtual Reality

Inside looking out or outside looking in?

A review on object pose recovery: From 3D bounding box detectors to full 6D pose estimators

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