Name
Abstract | ||
|---|---|---|
The task of ambiguous objects selection in 3D immersive Human-Computer interaction is fundamental and still challenging. The difficulty of precise selection of small/remote or occluded objects increased with the dense level of the virtual environment. In this paper, we present a guidance rays technique---a novel 3D selection method to address this issue, which allows user to perform distal pointing task with disambiguation mechanism depending on user's behavior. In guidance rays technique, total of three virtual rays are designed to select target, include one normal straight ray and two bendable rays. The paper focuses on strategies regarding effective, accurate selection of the intended target in VEs with light user fatigue. It's assumed that every single object in VEs has its own gravitational field and the guidance rays are bendable during the pointing task. The magnitudes of gravitational field of all the objects are sorted in descending order in every frame. Then the TOP2 ranking objects are indicated by two bendable guidance rays with different colors. Furthermore, to meet the need of dense virtual environment, a lock strategy is designed to avoid guidance ray switching between objects frequently. The experiments, carried out in ambiguous scenarios in which an absolute hand-held device with MEMS sensors was used as a selection tool, show that guidance rays technique is more precise than traditional 3D selection technique, and significantly efficient when selecting small/remote and occluded target. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2534329.2534347 | VRCAI |
Keywords | Field | DocType |
selection technique,selection method,precise selection,accurate selection,bendable ray,selection tool,bendable guidance ray,ambiguous objects selection,guidance rays technique,object selection,dense scenario,guidance ray,raycasting | Computer vision,Virtual machine,Ranking,Computer science,Gravitational field,Lock (computer science),Simulation,Mems sensors,Immersion (virtual reality),Artificial intelligence | Conference |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chongbin Xu | 1 | 23 | 5.10 |
| Mingquan Zhou | 2 | 266 | 65.04 |
| Dongdong Zhang | 3 | 0 | 0.34 |
| Wuyang Shui | 4 | 21 | 7.42 |
| Zhongke Wu | 5 | 255 | 52.70 |