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Crossing Rays: Evaluation of Bimanual Mid-air Selection Techniques in an Immersive Environment

DongHoon Kim, Dongyun Han, Siyeon Bak, Isaac Cho

TL;DR

The paper addresses the challenge of selecting mid-air destinations for immersive VR navigation by evaluating four crossing-ray, bimanual techniques—Simple-Ray, Simple-Stripe, Precision-Stripe, and Cursor-Sync—against an unimanual baseline across two task variants (with and without a reference). It systematically manipulates distance (3m, 6m, 9m) and uses two tasks to measure selection time and error distance, supplemented by NASA-TLX and UEQ questionnaires to capture workload and user experience. Across Task 1 with a reference, Simple-Ray often yields the fastest selection times, while Precision-Stripe and Cursor-Sync improve accuracy and user experience; Task 2 shows trade-offs where precision-oriented variants outperform in accuracy at longer distances at the expense of speed. Overall, bimanual techniques generally outperform the unimanual baseline, with selection-support features and visual aids enhancing accuracy and usability in reference-rich contexts, providing actionable guidance for designing mid-air navigation interfaces in VR.

Abstract

Mid-air navigation offers a method of aerial travel that mitigates the constraints associated with continuous navigation. A mid-air selection technique is essential to enable such navigation. In this paper, we consider four variations of intersection-based bimanual mid-air selection techniques with visual aids and supporting features: Simple-Ray, Simple-Stripe, Precision-Stripe, and Cursor-Sync. We evaluate their performance and user experience compared to an unimanual mid-air selection technique using two tasks that require selecting a mid-air position with or without a reference object. Our findings indicate that the bimanual techniques generally demonstrate faster selection times compared to the unimanual technique. With a supporting feature, the bimanual techniques can provide a more accurate selection than the unimanual technique. Based on our results, we discuss the effect of selection technique's visual aids and supporting features on performance and user experience for mid-air selection.

Crossing Rays: Evaluation of Bimanual Mid-air Selection Techniques in an Immersive Environment

TL;DR

The paper addresses the challenge of selecting mid-air destinations for immersive VR navigation by evaluating four crossing-ray, bimanual techniques—Simple-Ray, Simple-Stripe, Precision-Stripe, and Cursor-Sync—against an unimanual baseline across two task variants (with and without a reference). It systematically manipulates distance (3m, 6m, 9m) and uses two tasks to measure selection time and error distance, supplemented by NASA-TLX and UEQ questionnaires to capture workload and user experience. Across Task 1 with a reference, Simple-Ray often yields the fastest selection times, while Precision-Stripe and Cursor-Sync improve accuracy and user experience; Task 2 shows trade-offs where precision-oriented variants outperform in accuracy at longer distances at the expense of speed. Overall, bimanual techniques generally outperform the unimanual baseline, with selection-support features and visual aids enhancing accuracy and usability in reference-rich contexts, providing actionable guidance for designing mid-air navigation interfaces in VR.

Abstract

Mid-air navigation offers a method of aerial travel that mitigates the constraints associated with continuous navigation. A mid-air selection technique is essential to enable such navigation. In this paper, we consider four variations of intersection-based bimanual mid-air selection techniques with visual aids and supporting features: Simple-Ray, Simple-Stripe, Precision-Stripe, and Cursor-Sync. We evaluate their performance and user experience compared to an unimanual mid-air selection technique using two tasks that require selecting a mid-air position with or without a reference object. Our findings indicate that the bimanual techniques generally demonstrate faster selection times compared to the unimanual technique. With a supporting feature, the bimanual techniques can provide a more accurate selection than the unimanual technique. Based on our results, we discuss the effect of selection technique's visual aids and supporting features on performance and user experience for mid-air selection.
Paper Structure (53 sections, 6 figures, 4 tables)

This paper contains 53 sections, 6 figures, 4 tables.

Figures (6)

  • Figure 1: Crossing Rays: A) Simple-Ray: Two rays from each controller make an intersection point, pointing to a mid-air position; B) Simple-Stripe: The base is the Simple-Ray technique, and 1-meter length stripes are on the ray of the dominant hand side; C) Precision-Stripe: The intersection point can only be located in the selected range, one of the stripes; and D) Cursor-Sync: The selecting point's relative position in the selected range is synchronized with the position of the intersection point in the closest stripe. E) One-Hand Simultaneous: The unimanual mid-air selection with parabolic ray, which can control vertical position using a joystick on the controller pushing (upward) and pulling (downward).
  • Figure 2: A) Experiment room is a 15m width, depth, and height empty space with a 5m height pillar for a participant. B) Target position is located in front of the participant with three different distance conditions (3m, 6m, and 9m). The location is 10 degrees away from the central axis. C) In Task 1, the target position is 50cm in front of a reference object (Wooden box). The participant can only select within 50cm of the target position. D) In Task 2, a red sphere is a target position. It disappears 1.5 seconds after the start of each task.After the target has disappeared, the participant has to select the position where the red sphere was using a given technique.
  • Figure 3: Task 1 - Selection Time results with 95% CI: Lower values indicate better and faster performance, whereas higher values signify poorer performance.
  • Figure 4: Task 1 - Error Distance results with 95% CI: Lower values indicate accurate performance, while higher values are inaccurate.
  • Figure 5: Task 2 - Selection Time results with 95% CI. Lower values indicate better/faster performance, whereas higher values signify poorer performance.
  • ...and 1 more figures