Yao Wang, Qi Dai, Andreas Bulling, Mihai Bâce, Karsten Klein, Saliency3D
{"title":"Saliency3D: A 3D Saliency Dataset Collected on Screen","authors":"Yao Wang, Qi Dai, Andreas Bulling, Mihai Bâce, Karsten Klein, Saliency3D","doi":"10.1145/3649902.3653350","DOIUrl":null,"url":null,"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.","PeriodicalId":127538,"journal":{"name":"Eye Tracking Research & Application","volume":"6 1","pages":"19:1-19:6"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Eye Tracking Research & Application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3649902.3653350","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.