请减少晕屏症：揭秘并检测虚拟现实应用中的立体视觉不一致问题
Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality Apps
AI总结：
本研究针对VR应用中普遍存在但研究不足的立体视觉不一致（SVI）问题，提出无监督黑盒测试框架StereoID，通过深度感知条件立体图像转换器生成合成右眼图并计算与真实图的距离检测SVI，构建超17.1万张图像的数据集验证其优异检测性能。
摘要：
虚拟现实（VR）应用的质量至关重要，尤其是VR图形用户界面（GUI）的渲染质量。与传统2D应用不同，VR应用通过分别为用户的左右眼渲染两幅不同的2D图像，为用户创建3D数字场景。然而，立体视觉不一致（记为“SVI”）问题会干扰用户大脑的渲染过程，导致用户不适甚至不良健康影响。这类问题普遍存在，但尚未得到充分研究。我们对来自15个VR平台的282份SVI bug报告进行了实证分析，总结出15种表现形式。实证分析表明，自动检测SVI问题具有挑战性，主要原因在于：（1）缺乏训练数据；（2）SVI问题的表现形式多样、复杂，且通常具有应用特异性；（3）大多数可访问的VR应用都是闭源商业软件。现有的基于模式的监督分类方法可能不适用于或无法有效检测SVI问题。为应对这些挑战，我们提出了一种名为StereoID的无监督黑盒测试框架，仅基于渲染后的GUI状态来识别立体视觉不一致问题。StereoID根据实际的左眼图像生成合成右眼图像，并计算合成右眼图像与实际右眼图像之间的距离以检测SVI问题。我们提出了一种深度感知条件立体图像转换器来支撑图像生成过程，它能捕捉左右眼图像之间预期的视角偏移。我们构建了一个大规模未标注VR立体截图数据集，包含来自288个真实VR应用的超过17.1万张图像，用于实验。经过大量实验，StereoID在检测用户报告和真实VR应用中的SVI问题方面均表现出优异性能。
英文摘要：
The quality of Virtual Reality (VR) apps is vital, particularly the rendering quality of the VR Graphical User Interface (GUI). Different from traditional 2D apps, VR apps create a 3D digital scene for users, by rendering two distinct 2D images for the user's left and right eyes, respectively. Stereoscopic visual inconsistency (denoted as "SVI") issues, however, undermine the rendering process of the user's brain, leading to user discomfort and even adverse health effects. Such issues commonly exist but remain underexplored. We conduct an empirical analysis on 282 SVI bug reports from 15 VR platforms, summarizing 15 types of manifestations. The empirical analysis reveals that automatically detecting SVI issues is challenging, mainly because: (1) lack of training data; (2) the manifestations of SVI issues are diverse, complicated, and often application-specific; (3) most accessible VR apps are closed-source commercial software. Existing pattern-based supervised classification approaches may be inapplicable or ineffective in detecting the SVI issues. To counter these challenges, we propose an unsupervised black-box testing framework named StereoID to identify the stereoscopic visual inconsistencies, based only on the rendered GUI states. StereoID generates a synthetic right-eye image based on the actual left-eye image and computes distances between the synthetic right-eye image and the actual right-eye image to detect SVI issues. We propose a depth-aware conditional stereo image translator to power the image generation process, which captures the expected perspective shifts between left-eye and right-eye images. We build a large-scale unlabeled VR stereo screenshot dataset with larger than 171K images from 288 real-world VR apps for experiments. After substantial experiments, StereoID demonstrates superior performance for detecting SVI issues in both user reports and wild VR apps.
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