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Multi-view Stereopsis from Virtual Cameras

by Dao Lam and Ruizhi Hong

Introduction

In [1], we proposed a new method for 3D modeling that uses multiple virtual views from a single stereo pair. Our approach, while it is multi-view based, it does not require a large number of calibrated cameras positioned around the object. Instead, our method only requires a single pair of calibrated cameras and a on-the-fly motion detection algorithm that estimates the position of virtual cameras as the object moves with respect to the cameras. Besides its much lower cost, and despite the much simpler setup, the 3D models created using this approach are highly comparable to the original PMVS, while maintaining the same computational efficiency. Also, as the original PMVS, our method works well on various objects, including human faces, as we demonstrated in [1] and briefly present below.

 

(a)

(b)

Figure 1: (a) Proposed Framework for Virtual Multi-View 3D Modeling, (b) Real and Virtual Cameras:  L and R are real cameras; Lí and Rí are estimated poses of two virtual cameras due to the motion of the object O

 

Proposed Framework

Our framework for 3D object modeling consists of six majors steps. Figure 1a depicts such steps, which are: 1) Multiple pairs of stereo images are captured by 2 calibrated cameras while the object moves freely with respect to the cameras; 2) A SIFT-based feature extraction algorithm  establishes the correspondence between various points on every stereo pair sampled; 3) The intersection between the sets of points from two consecutive pairs of images is determined. That is, common feature points present in both the left-right image pair at camera-object position i and the subsequent left-right image pair at camera-object position i+1 are identified; 4) The 3D coordinates of every point in the intersection above is calculated; 5) The transformation between camera-object poses are estimated using the 3D coordinates of the intersection; and 6) The previous transformations are used to create virtual poses of the camera (Figure 1b) and fed into a patched-base multi-view software to construct the 3D model of the object.

Results

 

(c)

(a)

(d)

(f)

(b)

 

(e)

(g)

Figure 2: Quantitative and Qualitative Results obtained using human body and two objects: (a) and (b) shows the 3D model of a face using respectively 16 and 70 low-resolution images; (c) three views of the 3D model created for the human upper body also using low-resolution images; (d) and (f) shows the images of the angel and bunny used for testing; (e) and (g) show the 3D model created using higher-resolution cameras.

 

 

Number of views

Error (mm)

Percentage of points

with error < 1mm

Percentage of points

with error < 1.5mm

Angel

7

0.49

89.00%

96.00%

Bunny

12

1.24

85.50%

93.40%

Table 1: Accuracy of the proposed method for 3D Modeling

 

3D Model created using Virtual Cameras
(click on the image to play the video)

    References

  1. Lam, D., Hong, R, and DeSouza, G. N., "3D Human Modeling using Virtual Multi-View Stereopsis and Motion Estimation", in Proceedings of the 2009 IEEE International Conference on Robotic System (IROS), pp. 4294-4299, Oct./09
     
  2. Park J., DeSouza G.N., " Photorealistic Modeling of Three Dimensional Objects Using Range and Reflectance Data", in Innovations in Machine Intelligence and Robot Perception , Edited by: S. Patnaik, L.C. Jain, G. Tzafestas and V. Bannore, © Springer-Verlag (2005).

 

 

 

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