Label Guidance based Object Locating in Virtual Reality

TL;DR

This paper introduces a label guidance system for object locating in virtual reality that enhances efficiency and user comfort by using hierarchical label layouts and dynamic flying label guidance.

Contribution

It proposes a novel label guidance pipeline with hierarchical layout principles and a view and gaze-based guidance method for improved VR object locating.

Findings

Significantly improved locating efficiency

Reduced task load during object locating

Effective guidance with real-time label updates

Abstract

Object locating in virtual reality (VR) has been widely used in many VR applications, such as virtual assembly, virtual repair, virtual remote coaching. However, when there are a large number of objects in the virtual environment(VE), the user cannot locate the target object efficiently and comfortably. In this paper, we propose a label guidance based object locating method for locating the target object efficiently in VR. Firstly, we introduce the label guidance based object locating pipeline to improve the efficiency of the object locating. It arranges the labels of all objects on the same screen, lets the user select the target labels first, and then uses the flying labels to guide the user to the target object. Then we summarize five principles for constructing the label layout for object locating and propose a two-level hierarchical sorted and orientated label layout based on the five principles for the user to select the candidate labels efficiently and comfortably. After that, we propose the view and gaze based label guidance method for guiding the user to locate the target object based on the selected candidate labels.It generates specific flying trajectories for candidate labels, updates the flying speed of candidate labels, keeps valid candidate labels , and removes the invalid candidate labels in real time during object locating with the guidance of the candidate labels. Compared with the traditional method, the user study results show that our method significantly improves efficiency and reduces task load for object locating.