Saliency3D: A 3D Saliency Dataset Collected on Screen

Yao Wang, Qi Dai, Andreas Bulling, Mihai Bâce, Karsten Klein, Saliency3D
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Abstract

While visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilises an eye tracker on a screen to collect 3D saliency data, which could reduce the cost and complexity of data collection. We first collected gaze data on a computer screen and then mapped the 2D points to 3D saliency data through perspective transformation. Using this method, we propose Saliency3D, a 3D saliency dataset (49,276 fixations) comprising 10 participants looking at sixteen objects. We examined the viewing preferences for objects and our results indicate potential preferred viewing directions and a correlation between salient features and the variation in viewing directions.
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Saliency3D:屏幕上收集的三维显著性数据集
虽然最近有人对三维视觉显著性进行了研究,但收集三维显著性数据的实验装置可能既昂贵又繁琐。为解决这一难题,我们提出了一种新颖的实验设计,利用屏幕上的眼动追踪器来收集三维突出数据,从而降低数据收集的成本和复杂性。我们首先在计算机屏幕上收集注视数据,然后通过透视变换将二维点映射为三维突出度数据。利用这种方法,我们提出了 Saliency3D 三维突出数据集(49,276 个固定点),该数据集由 10 名观察 16 个物体的参与者组成。我们研究了观察对象的偏好,结果显示了潜在的偏好观察方向,以及突出特征与观察方向变化之间的相关性。
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