For many practical applications in industrial and medical fields, 3D object recognition based on feature extracting has become an actively investigative field. In general, 3D object recognition system can be completed through two stages. Firstly, we use 3D reconstruction of input data to get object expression. Then we use 3D invariant to identify objects. In order to promote further development of this field, this paper summarizes the 3D object recognition technology research, and introduces typical methods of modern 3D object recognition, the concepts of 3D reconstruction, 3D invariants. In the process of 3D object recognition, feature line extraction, 3D invariant calculation, matching strategy, 3D object recognition fields are reviewed. The first section presents the significance of 3D object recognition. The second section presents the development of object recognition. The third section presents the double viewpoint of 3D reconstruction. The fourth section presents the 3D reconstruction of image processing. The fifth section presents the 3D invariant technology. The sixth section presents the problems and development direction.
[1]
A. Ramachandra Rao,et al.
Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering
,
2008
.
[2]
Jim Austin,et al.
Three-dimensional face recognition using combinations of surface feature map subspace components
,
2008,
Image Vis. Comput..
[3]
Z. Rongchun.
Geometric Invariance and Its Applications to 3D Object Recognition
,
2003
.
[4]
Yuan-Luo,et al.
3D object recognition technique based on muti-resolution aspect graph
,
2005,
2005 International Conference on Neural Networks and Brain.
[5]
Robert P. Goldman,et al.
A Bayesian Model of Plan Recognition
,
1993,
Artif. Intell..
[6]
Tsorng-Lin Chia,et al.
Using cross-ratios to model curve data for aircraft recognition
,
2003,
Pattern Recognit. Lett..