IEEE Transactions on Visualization and Computer Graphics最新文献

We present a method for adding parallax and real-time playback of 360° videos in Virtual Reality headsets. In current video players, the playback does not respond to translational head movement, which reduces the feeling of immersion, and causes motion sickness for some viewers. Given a 360° video and its corresponding depth (provided by current stereo 360° stitching algorithms), a naive image-based rendering approach would use the depth to generate a 3D mesh around the viewer, then translate it appropriately as the viewer moves their head. However, this approach breaks at depth discontinuities, showing visible distortions, whereas cutting the mesh at such discontinuities leads to ragged silhouettes and holes at disocclusions. We address these issues by improving the given initial depth map to yield cleaner, more natural silhouettes. We rely on a three-layer scene representation, made up of a foreground layer and two static background layers, to handle disocclusions by propagating information from multiple frames for the first background layer, and then inpainting for the second one. Our system works with input from many of today's most popular 360° stereo capture devices (e.g., Yi Halo or GoPro Odyssey), and works well even if the original video does not provide depth information. Our user studies confirm that our method provides a more compelling viewing experience than without parallax, increasing immersion while reducing discomfort and nausea.

Creating metrically accurate avatars is important for many applications such as virtual clothing try-on, ergonomics, medicine, immersive social media, telepresence, and gaming. Creating avatars that precisely represent a particular individual is challenging however, due to the need for expensive 3D scanners, privacy issues with photographs or videos, and difficulty in making accurate tailoring measurements. We overcome these challenges by creating "The Virtual Caliper", which uses VR game controllers to make simple measurements. First, we establish what body measurements users can reliably make on their own body. We find several distance measurements to be good candidates and then verify that these are linearly related to 3D body shape as represented by the SMPL body model. The Virtual Caliper enables novice users to accurately measure themselves and create an avatar with their own body shape. We evaluate the metric accuracy relative to ground truth 3D body scan data, compare the method quantitatively to other avatar creation tools, and perform extensive perceptual studies. We also provide a software application to the community that enables novices to rapidly create avatars in fewer than five minutes. Not only is our approach more rapid than existing methods, it exports a metrically accurate 3D avatar model that is rigged and skinned.

Being able to hear objects in an environment, for example using echolocation, is a challenging task. The main goal of the current work is to use virtual environments (VEs) to train novice users to navigate using echolocation. Previous studies have shown that musicians are able to differentiate sound pulses from reflections. This paper presents design patterns for VE simulators for both training and testing procedures, while classifying users' navigation strategies in the VE. Moreover, the paper presents features that increase users' performance in VEs. We report the findings of two user studies: a pilot test that helped improve the sonic interaction design, and a primary study exposing participants to a spatial orientation task during four conditions which were early reflections (RF), late reverberation (RV), early reflections-reverberation (RR) and visual stimuli (V). The latter study allowed us to identify navigation strategies among the users. Some users (10/26) reported an ability to create spatial cognitive maps during the test with auditory echoes, which may explain why this group performed better than the remaining participants in the RR condition.

Virtual Environments (VEs) provide the opportunity to simulate a wide range of applications, from training to entertainment, in a safe and controlled manner. For applications which require realistic representations of real world environments, the VEs need to provide multiple, physically accurate sensory stimuli. However, simulating all the senses that comprise the human sensory system (HSS) is a task that requires significant computational resources. Since it is intractable to deliver all senses at the highest quality, we propose a resource distribution scheme in order to achieve an optimal perceptual experience within the given computational budgets. This paper investigates resource balancing for multi-modal scenarios composed of aural, visual and olfactory stimuli. Three experimental studies were conducted. The first experiment identified perceptual boundaries for olfactory computation. In the second experiment, participants ( N=25) were asked, across a fixed number of budgets ( M=5), to identify what they perceived to be the best visual, acoustic and olfactory stimulus quality for a given computational budget. Results demonstrate that participants tend to prioritize visual quality compared to other sensory stimuli. However, as the budget size is increased, users prefer a balanced distribution of resources with an increased preference for having smell impulses in the VE. Based on the collected data, a quality prediction model is proposed and its accuracy is validated against previously unused budgets and an untested scenario in a third and final experiment.

We present a display for optical see-through near-eye displays based on light attenuation, a new paradigm that forms images by spatially subtracting colors of light. Existing optical see-through head-mounted displays (OST-HMDs) form virtual images in an additive manner-they optically combine the light from an embedded light source such as a microdisplay into the users' field of view (FoV). Instead, our light attenuation display filters the color of the real background light pixel-wise in the users' see-through view, resulting in an image as a spatial color filter. Our image formation is complementary to existing light-additive OST-HMDs. The core optical component in our system is a phase-only spatial light modulator (PSLM), a liquid crystal module that can control the phase of the light in each pixel. By combining PSLMs with polarization optics, our system realizes a spatially programmable color filter. In this paper, we introduce our optics design, evaluate the spatial color filter, consider applications including image rendering and FoV color control, and discuss the limitations of the current prototype.

This paper presents the implementation and evaluation of a 50,000-pose-sample-per-second, 6-degree-of-freedom optical head tracking instrument with motion-to-pose latency of 28μs and dynamic precision of 1-2 arcminutes. The instrument uses high-intensity infrared emitters and two duo-lateral photodiode-based optical sensors to triangulate pose. This instrument serves two purposes: it is the first step towards the requisite head tracking component in sub- 100μs motion-to-photon latency optical see-through augmented reality (OST AR) head-mounted display (HMD) systems; and it enables new avenues of research into human visual perception - including measuring the thresholds for perceptible real-virtual displacement during head rotation and other human research requiring high-sample-rate motion tracking. The instrument's tracking volume is limited to about 120×120×250 but allows for the full range of natural head rotation and is sufficient for research involving seated users. We discuss how the instrument's tracking volume is scalable in multiple ways and some of the trade-offs involved therein. Finally, we introduce a novel laser-pointer-based measurement technique for assessing the instrument's tracking latency and repeatability. We show that the instrument's motion-to-pose latency is 28μs and that it is repeatable within 1-2 arcminutes at mean rotational velocities (yaw) in excess of 500°/sec.

In this paper, we propose a three-dimensional (3D) convolutional neural network (CNN)-based method for predicting the degree of motion sickness induced by a 360° stereoscopic video. We consider the user's eye movement as a new feature, in addition to the motion velocity and depth features of a video used in previous work. For this purpose, we use saliency, optical flow, and disparity maps of an input video, which represent eye movement, velocity, and depth, respectively, as the input of the 3D CNN. To train our machine-learning model, we extend the dataset established in the previous work using two data augmentation techniques: frame shifting and pixel shifting. Consequently, our model can predict the degree of motion sickness more precisely than the previous method, and the results have a more similar correlation to the distribution of ground-truth sickness.

Drawings of non-planar graphs always result in edge crossings. When there are many edges crossing at small angles, it is often difficult to follow these edges, because of the multiple visual paths resulted from the crossings that slow down eye movements. In this paper we propose an algorithm that disambiguates the edges with automatic selection of distinctive colors. Our proposed algorithm computes a near optimal color assignment of a dual collision graph, using a novel branch-and-bound procedure applied to a space decomposition of the color gamut. We give examples demonstrating this approach in real world graphs and maps, as well as a user study to establish its effectiveness and limitations.