Estimating 3D spatiotemporal point of regard: a device evaluation

被引:0
|
作者
Wagner, Peter [1 ,2 ]
Ho, Arthur [1 ,2 ]
Kim, Juno [2 ]
机构
[1] Brien Holden Vis Inst Ltd, Lv 4,RMB North Wing,14 Barker Str, Sydney, NSW 2052, Australia
[2] Univ New South Wales, Sch Optometry & Vis Sci, Lv 3,RMB North Wing,14 Barker Str, Sydney, NSW 2052, Australia
关键词
PIVOT POINT; EYE; LOCATION; PUPIL; ACCURACY; TRACKING; HUMANS; MODEL;
D O I
10.1364/JOSAA.457663
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
This paper presents and evaluates a system and method that record spatiotemporal scene information and location of the center of visual attention, i.e., spatiotemporal point of regard (PoR) in ecological environments. A primary research application of the proposed system and method is for enhancing current 2D visual attention models. Current eye-tracking approaches collapse a scene's depth structures to a 2D image, omitting visual cues that trigger important functions of the human visual system (e.g., accommodation and vergence). We combined head-mounted eye-tracking with a miniature time-of-flight camera to produce a system that could be used to estimate the spa-tiotemporal location of the PoR-the point of highest visual attention-within 3D scene layouts. Maintaining calibration accuracy is a primary challenge for gaze mapping; hence, we measured accuracy repeatedly by matching the PoR to fixated targets arranged within a range of working distances in depth. Accuracy was estimated as the deviation from estimated PoR relative to known locations of scene targets. We found that estimates of 3D PoR had an overall accuracy of approximately 2 degrees omnidirectional mean average error (OMAE) with variation over a 1 h recording maintained within 3.6 degrees OMAE. This method can be used to determine accommodation and vergence cues of the human visual system continuously within habitual environments, including everyday applications (e.g., use of hand-held devices). (c) 2022 Optica Publishing Group
引用
收藏
页码:1343 / 1351
页数:9
相关论文
共 50 条
  • [21] STModelViz: A 3D spatiotemporal GIS using a constraint-based approach
    Li, Jing
    Wong, David W. S.
    COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2014, 45 : 34 - 49
  • [22] Top-Down Tracking and Estimating 3D Pose of a Die
    Torres, Fuensanta
    Kropatsch, Walter G.
    STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, 2012, 7626 : 492 - 500
  • [23] Resolving 3D Human Pose Ambiguities with 3D Scene Constraints
    Hassan, Mohamed
    Choutas, Vasileios
    Tzionas, Dimitrios
    Black, Michael J.
    2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, : 2282 - 2292
  • [24] 3D Registration of Indoor Point Clouds for Augmented Reality
    Mahmood, Bilawal
    Han, SangUk
    COMPUTING IN CIVIL ENGINEERING 2019: VISUALIZATION, INFORMATION MODELING, AND SIMULATION, 2019, : 1 - 8
  • [25] Optimal Point Spread Function Design for 3D Imaging
    Shechtman, Yoav
    Sahl, Steffen J.
    Backer, Adam S.
    Moerner, W. E.
    PHYSICAL REVIEW LETTERS, 2014, 113 (13)
  • [26] Perceptual Quality Assessment of Colored 3D Point Clouds
    Liu, Qi
    Su, Honglei
    Duanmu, Zhengfang
    Liu, Wentao
    Wang, Zhou
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2023, 29 (08) : 3642 - 3655
  • [27] Bioinspired point cloud representation: 3D object tracking
    Orts-Escolano, Sergio
    Garcia-Rodriguez, Jose
    Cazorla, Miguel
    Morell, Vicente
    Azorin, Jorge
    Saval, Marcelo
    Garcia-Garcia, Alberto
    Villena, Victor
    NEURAL COMPUTING & APPLICATIONS, 2018, 29 (09) : 663 - 672
  • [28] 3D Lidar Point Cloud Segmentation for Automated Driving
    Abbasi, Rashid
    Bashir, Ali Kashif
    Rehman, Amjad
    Ge, Yuan
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2025, 17 (01) : 8 - 29
  • [29] Compression and registration of 3D point clouds using GMMs
    Navarrete, Javier
    Viejo, Diego
    Cazorla, Miguel
    PATTERN RECOGNITION LETTERS, 2018, 110 : 8 - 15
  • [30] 3D point cloud reconstruction using panoramic images
    Sharma, Surendra Kumar
    Jain, Kamal
    Shukla, Anoop Kumar
    APPLIED GEOMATICS, 2024, 16 (03) : 575 - 592