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Reconstruction of three-dimensional (3D) scenes is an active research topic in the field of computer vision and 3D
display. It’s a challenge to model 3D objects rapidly and effectively. A 3D model can be extracted from multiple images.
The system only requires a sequence of images taken with cameras without knowing the parameters of camera, which
provide flexibility to a high degree. We focus on quickly merging point cloud of the object from depth map sequences.
The whole system combines algorithms of different areas in computer vision, such as camera calibration, stereo
correspondence, point cloud splicing and surface reconstruction. The procedure of 3D reconstruction is decomposed into
a number of successive steps. Firstly, image sequences are received by the camera freely moving around the object.
Secondly, the scene depth is obtained by a non-local stereo matching algorithm. The pairwise is realized with the Scale
Invariant Feature Transform (SIFT) algorithm. An initial matching is then made for the first two images of the sequence.
For the subsequent image that is processed with previous image, the point of interest corresponding to ones in previous
images are refined or corrected. The vertical parallax between the images is eliminated. The next step is to calibrate
camera, and intrinsic parameters and external parameters of the camera are calculated. Therefore, The relative position
and orientation of camera are gotten. A sequence of depth maps are acquired by using a non-local cost aggregation
method for stereo matching. Then point cloud sequence is achieved by the scene depths, which consists of point cloud
model using the external parameters of camera and the point cloud sequence. The point cloud model is then
approximated by a triangular wire-frame mesh to reduce geometric complexity and to tailor the model to the
requirements of computer graphics visualization systems. Finally, the texture is mapped onto the wire-frame model,
which can also be used for 3D display. According to the experimental results, we can reconstruct a 3D point cloud model
more quickly and efficiently than other methods.
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