Data from: The (in)effectiveness of simulated blur for depth perception in naturalistic images

We examine depth perception in images of real scenes with naturalistic variation in pictorial depth cues, simulated dioptric blur and binocular disparity. Light field photographs of natural scenes were taken with a Lytro plenoptic camera that simultaneously captures images at up to 12 focal planes. When accommodation at any given plane was simulated, the corresponding defocus blur at other depth planes was extracted from the stack of focal plane images. Depth information from pictorial cues, relative blur and stereoscopic disparity was separately introduced into the images. In 2AFC tasks, observers were required to indicate which of two patches extracted from these images was farther. Depth discrimination sensitivity was highest when geometric and stereoscopic disparity cues were both present. Blur cues impaired sensitivity by reducing the contrast of geometric information at high spatial frequencies. While simulated generic blur may not assist depth perception, it remains possible that dioptric blur from the optics of an observer's own eyes may be used to recover depth information on an individual basis. The implications of our findings for virtual reality rendering technology are discussed.本研究探讨了包含自然变化的图画深度线索、模拟屈光模糊（dioptric blur）与双眼视差（binocular disparity）的真实场景图像中的深度知觉。研究采用Lytro光场相机（Lytro plenoptic camera）拍摄自然场景的光场照片，该相机可同时捕捉最多12个焦平面的图像。当模拟任意给定焦平面的视觉调节时，可从焦平面图像堆栈中提取其他深度平面上对应的散焦模糊信息。研究分别将来自图画深度线索、相对模糊与立体视差的深度信息引入目标图像中。在二择一迫选任务（2AFC）中，观察者需指出从这些图像中提取的两个图像区块中哪一个距离更远。当几何线索与立体视差线索同时存在时，深度辨别灵敏度达到最高水平。模糊线索会通过降低高空间频率下的几何信息对比度，损害深度辨别灵敏度。尽管模拟的通用模糊或许无法辅助深度知觉，但仍有可能利用观察者自身眼睛光学系统产生的屈光模糊，基于个体层面恢复深度信息。本研究还讨论了本次发现对虚拟现实渲染技术的启示意义。