SCIENCE CHINA Information Sciences, Volume 63, Issue 1: 112101(2020) https://doi.org/10.1007/s11432-019-1517-3

A flexible technique to select objects via convolutional neural network in VR space

• AcceptedAug 1, 2019
• PublishedDec 23, 2019
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Abstract

Most studies on the selection techniques of projection-based VR systems are dependent on users wearing complex or expensive input devices, however there are lack of more convenient selection techniques. In this paper, we propose a flexible 3D selection technique in a large display projection-based virtual environment. Herein, we present a body tracking method using convolutional neural network (CNN) to estimate 3D skeletons of multi-users, and propose a region-based selection method to effectively select virtual objects using only the tracked fingertips of multi-users. Additionally, a multi-user merge method is introduced to enable users' actions and perception to realign when multiple users observe a single stereoscopic display. By comparing with state-of-the-art CNN-based pose estimation methods, the proposed CNN-based body tracking method enables considerable estimation accuracy with the guarantee of real-time performance. In addition, we evaluate our selection technique against three prevalent selection techniques and test the performance of our selection technique in a multi-user scenario. The results show that our selection technique significantly increases the efficiency and effectiveness, and is of comparable stability to support multi-user interaction.

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• Figure 1

(Color online) The illustration of our technique (once the object is selected, the contour is marked): (a) the user points at the desired object with his fingertip; (b) the object is selected when the fingertip occludes part of the object.

• Figure 2

(Color online) Architecture of the two-stage body tracking method. Using the multiple sub-stage CNN with two branches, the method eventually estimates correct full body poses for each user in the depth image.

• Figure 3

(Color online) The process of projecting a fingertip region onto the projection plane. The projection region of the fingertip can be considered the “shadow" of the fingertip with the eye position.

• Figure 4

(Color online) The progressive refinement in selection. (a) Several selectable objects are detected; (b) the objects are zoomed in for convenience of selection.

• Figure 5

(Color online) Multi-users' interaction in the virtual environment is shown in (a). The individual visions of the two users are respectively shown in (b) and (c).

• Figure 6

(Color online) Implementation of Ray Cast, Cone Cast and SQUAD in the virtual environment. (a) Ray Cast technique; (b) Cone Cast technique; (c) SQUAD and the Quad-menu.

• Figure 7

(Color online) The four different scenarios used in study one. (a) The scene of Scenario 1; (b) the scene of Scenario 2; (c) the scene of Scenario 3; (d) the scene of Scenario 4.

• Figure 8

(Color online) The mean completion time (a) and error number (b) for the four techniques in each scenario.

• Figure 9

(Color online) Illustration of multi-user collaboration (a) in the virtual assembly application. (b) The mean number of incorrect joint connections of each method for each scenario.

• Figure 10

(Color online) Comparison between the trajectories of the user's head tracked by our body tracking method and by Kinect SDK. (a) The values of $X$ coordinate; (b) the values of $Y$ coordinate; (c) the values of $Z$ coordinate.

• Figure 11

(Color online) Our method succeeds in mutual occlusion (b) and (d), while the estimates of Kinect SDK are erroneous (a) and (c).

• Table 1   Comparison of mAP (%) and time (s/frame) on the full testing set of MPII multi-person dataset$^{\rm~a)}$
 Method Head Shoulder Elbow Wrist Hip Knee Ankle Total Time DeeperCut [17] 78.4 72.5 60.2 51.0 57.2 52.0 45.4 59.5 485 Iqbal et al. [18] 58.4 53.9 44.5 35.0 42.2 36.7 31.1 43.1 10 CMU-Pose [16] 91.2 87.6 77.7 66.8 75.4 68.9 61.7 75.6 1.24 RMPE [19] 88.4 86.5 78.6 70.4 74.4 73.0 65.8 76.7 1.5 Ours 91.7 87.9 78.3 68.7 75.2 74.1 64.3 77.2 1.05

a

• Table 2   Comparison on the testing subset test-dev of the COCO dataset$^{\rm~a)b)}$
 Method AP(%) ${\rm{A}}{{\rm{P}}^{50}}$(%) ${\rm{A}}{{\rm{P}}^{75}}$(%) ${\rm{A}}{{\rm{P}}^{\rm~M}}$(%) ${\rm{A}}{{\rm{P}}^{\rm~L}}$(%) Time (s/frame) CMU-Pose [16] 61.8 84.9 67.5 57.1 68.2 0.1 RMPE [19] 61.8 83.7 69.8 58.6 67.6 2.5 Ours 63.3 85.3 68.9 57.8 68.8 0.08
• Table 3   Comparison of mAP (%) and time (s/frame) on the full testing set of MPII multi-person dataset$^{\rm~a)}$
 Method Head Shoulder Elbow Wrist Hip Knee Ankle Finger Total Time CMU-Pose [16] 90.3 86.5 74.7 64.2 74.3 70.2 62.3 77.4 74.9 0.56 Ours 89.8 87.4 76.2 65.7 73.8 73.1 63.5 79.7 76.1 0.13

a

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