Department of Psychology and Centre for Vision Research, York University, Canada.
Department of Psychology and Centre for Vision Research, York University, Canada.
Vision Res. 2021 Nov;188:51-64. doi: 10.1016/j.visres.2021.07.003. Epub 2021 Jul 18.
Motion parallax and binocular disparity contribute to the perceived depth of three-dimensional (3D) objects. However, depth is often misperceived, even when both cues are available. This may be due in part to conflicts with unmodelled cues endemic to computerized displays. Here we evaluated the impact of display-based cue conflicts on depth cue integration by comparing perceived depth for physical and virtual objects. Truncated square pyramids were rendered using Blender and 3D printed. We assessed perceived depth using a discrimination task with motion parallax, binocular disparity, and their combination. Physical stimuli were presented with precise control over position and lighting. Virtual stimuli were viewed using a head-mounted display. To generate motion parallax, observers made lateral head movements using a chin rest on a motion platform. Observers indicated if the width of the front face appeared greater or less than the distance between this surface and the base. We found that accuracy was similar for virtual and physical pyramids. All estimates were more precise when depth was defined by binocular disparity than motion parallax. Our probabilistic model shows that a linear combination model does not adequately describe performance in either physical or virtual conditions. While there was inter-observer variability in weights, performance in all conditions was best predicted by a veto model that excludes the less reliable depth cue, in this case motion parallax.
运动视差和双目视差有助于感知三维(3D)物体的深度。然而,即使有两种线索,深度也常常被误解。这可能部分是由于与计算机化显示器特有的未建模线索冲突所致。在这里,我们通过比较物理和虚拟对象的感知深度来评估基于显示的线索冲突对深度线索整合的影响。使用 Blender 渲染截断的方锥形,并使用 3D 打印机进行打印。我们使用具有运动视差、双目视差及其组合的辨别任务来评估感知深度。物理刺激物的呈现可以精确控制位置和照明。虚拟刺激物通过头戴式显示器观看。为了产生运动视差,观察者使用运动平台上的下巴托进行侧向头部运动。观察者指出前面的宽度看起来大于还是小于这个表面和底座之间的距离。我们发现虚拟和物理金字塔的准确性相似。当深度由双目视差定义时,所有估计值都比运动视差更精确。我们的概率模型表明,线性组合模型不能充分描述物理或虚拟条件下的性能。虽然观察者之间存在差异,但在所有条件下,排除不太可靠的深度线索(在此情况下为运动视差)的否决模型都能很好地预测性能。