Saliency3D:屏幕上收集的三维显著性数据集

Yao Wang, Qi Dai, Andreas Bulling, Mihai Bâce, Karsten Klein, Saliency3D
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引用次数: 0

摘要

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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Saliency3D: A 3D Saliency Dataset Collected on Screen
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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