A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Extension of the ICP algorithm to nonrigid intensity-based registration of 3D volumes
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Reconstruction of Freeform Objects with 3D SOM Neural Network Grids
PG '01 Proceedings of the 9th Pacific Conference on Computer Graphics and Applications
3D object reconstruction and representation using neural networks
Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
Quadric-based simplification in any dimension
ACM Transactions on Graphics (TOG)
Point fingerprint: a new 3-D object representation scheme
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rigid 3-D motion estimation using neural networks and initially estimated 2-D motion data
IEEE Transactions on Circuits and Systems for Video Technology
Artificial neural networks for 3-D motion analysis. I. Rigid motion
IEEE Transactions on Neural Networks
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3D surface registration is commonly used in shape analysis, surface representation, and medical image aided surgery. This technique is extremely computationally expensive and sometimes will lead to bad result configured with unstructured mass data for its’ iterative searching procedure and ill-suited distance function. In this paper, we propose a novel neural network strategy for surface registration. Before that, a typical preprocessing procedure-mesh PCA is used for coordinate direction normalization. The results and comparisons show such neural network method is a promising approach for 3D shape matching.