Overcoming eye-hand visibility mismatch in 3D pointing selection

Argelaguet, Ferran; Andújar, Carlos; Trueba, Ramón

doi:10.1145/1450579.1450588

Cited by 33 publications

(19 citation statements)

References 8 publications

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“…One class are hand rooted techniques and the others are eye rooted techniques. These are invented by Argelaguet et al [AAT08]. We will not be that specific to eye rooted, because it is hard to detect eye orientation.…”

Section: Ray Cast Techniquesmentioning

confidence: 99%

Modeling Distant Pointing for Compensating Systematic Displacements

Mayer

Wolf

Schneegaß

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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People use gestures to give verbal communication more expression and also to replace the speech. One of the most concise and expressive gestures is the pointing gesture. Pointing gesture can be observed in the early childhood. In these early years, it is used to point at objects or people. Later, people use pointing gestures even for more complex things such as to visualize directions. Increasing pointing gestures are also used for interacting with computers. For example, gestures can be used to remotely interact with a display, without using an input tool. In this work we investigated how people point to objects and how the recognition accuracy can be improved by using a gesture recognition system. We performed a user study, where participants had to point on projected pointing targets. These gestures were recorded as reference data with the help of motion capture system. We used a variety of starting positions in which the study was carried out. For this the participants were placed with a distance of 2 to 3 meters to the pointing targets. At these two positions the participants had to sit and stand while pointing to the targets. From the recorded reference data we derived a pointing vector. Each vector describes the direction in which the gesture is directed. We generate these vectors out of different body parts. This is done to show that there are different ways to create these vectors but they behave all the same. In the optimal case, this vector would describe the path of the person pointing to the object, in this case, the projected point. By mathematical analyzes we show that in average over several experiments and over several participants a systematic deviation from this optimal vector can be detected. We specify models, which can compensate the systematic deviation. These models shift the pointing vector in the direction of the average distance between optimum and average study vector. Products of the consumer market can be used to detect pointing gestures. There gestures can be improved with the generated models. The focus here is, for example, products like the Kinect.

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Section: Ray Cast Techniquesmentioning

confidence: 99%

Modeling Distant Pointing for Compensating Systematic Displacements

Mayer

Wolf

Schneegaß

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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“…First, the selected object may not be visible from the user's view, while no obstructions exist along the selection ray, leading to unintentional selections. This eye-hand visibility mismatch is tackled by raycasting from the eye [AAT08], while other techniques such as the selection based only on eye movements [CSD03,TJ00] circumvent it entirely. Second, a desired object can be (partially) obscured by thirdparty objects, making first-hit-raycasting selection difficult.…”

Section: D Object Selection In Vr Environmentsmentioning

confidence: 99%

“…Techniques lacking such controls which do not allow the users to create their own selection volume would have a negative impact on the variety of possible selections. Similarly, techniques that provide control over the origin of the selection tool only [ZBM94] are necessarily more limiting than techniques also providing orientation control [AAT08,FHZ96].…”

Section: Selection Tool Dofmentioning

confidence: 99%

Hybrid Touch/Tangible Spatial 3D Data Selection

Besançon

Sereno

et al. 2019

Computer Graphics Forum

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We discuss spatial selection techniques for three‐dimensional datasets. Such 3D spatial selection is fundamental to exploratory data analysis. While 2D selection is efficient for datasets with explicit shapes and structures, it is less efficient for data without such properties. We first propose a new taxonomy of 3D selection techniques, focusing on the amount of control the user has to define the selection volume. We then describe the 3D spatial selection technique Tangible Brush, which gives manual control over the final selection volume. It combines 2D touch with 6‐DOF 3D tangible input to allow users to perform 3D selections in volumetric data. We use touch input to draw a 2D lasso, extruding it to a 3D selection volume based on the motion of a tangible, spatially‐aware tablet. We describe our approach and present its quantitative and qualitative comparison to state‐of‐the‐art structure‐dependent selection. Our results show that, in addition to being dataset‐independent, Tangible Brush is more accurate than existing dataset‐dependent techniques, thus providing a trade‐off between precision and effort.

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“…Our work builds on three main relevant strands of prior work: (1) Gaze-based interaction in VR, (2) Interaction with menus in VR, and (3) Visual alignment for interaction.…”

Section: Related Workmentioning

confidence: 99%

“…Raycasting from the eye: Argelaguet et al [2] showed that compared to laser rayscasting, the selection performance in cluttered environments can be significantly improved when the wrist rotations are used for changing the orientation of a virtual ray which is cast from the eye position.…”

Section: Gaze Interaction In Vrmentioning

confidence: 99%

EyeSeeThrough: Unifying Tool Selection and Application in Virtual Environments

Mardanbegi

Mayer

Pfeuffer

et al. 2019

2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

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In 2D interfaces, actions are often represented by fixed tools arranged in menus, palettes, or dedicated parts of a screen, whereas 3D interfaces afford their arrangement at different depths relative to the user and the user can move them relative to each other. In this paper we introduce EyeSeeThrough as a novel interaction technique that utilises eye-tracking in VR. The user can apply an action to an intended object by visually aligning the object with the tool at the line-of-sight, and then issue a confirmation command. The underlying idea is to merge the two-step process of 1) selection of a mode in a menu and 2) applying it to a target, into one unified interaction. We present a user study where we compare the method to the baseline two-step selection. The results of our user study showed that our technique outperforms the two step selection in terms of speed and comfort. We further developed a prototype of a virtual living room to demonstrate the practicality of the proposed technique.

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Overcoming eye-hand visibility mismatch in 3D pointing selection

Cited by 33 publications

References 8 publications

Modeling Distant Pointing for Compensating Systematic Displacements

Modeling Distant Pointing for Compensating Systematic Displacements

Hybrid Touch/Tangible Spatial 3D Data Selection

EyeSeeThrough: Unifying Tool Selection and Application in Virtual Environments

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