Research On 3D Surface Reconstruction Algorithm Based On Compressed Sensing

With the rapid development of information technology,surgical training simulation system based on Virtual Reality(VR)provides a powerful tool for both surgical training and its evaluation,in which the accurate acquisition of soft issue's three-dimensional(3-D)structural information and physical characteristics ensures the authenticity,accuracy of virtual surgery simulation.In traditional methods,the method that through 3-D color scanner to get point data reconstructing soft issue is very expensive,not practical;while the method that sampling feature points in turn is tedious and complicated and this method can only roughly reconstruct soft issue which can't meet the requirement of the virtual surgery on the accuracy of the data.Therefore,how to streamline the process of obtaining the 3-D coordinates of points,reducing the amount of data collected and accurately reconstructing the 3-D soft tissue surface is a problem worthy of study.A method of 3-D tissue surface reconstruction based on Compressed Sensing(CS)and Cubic B-spline Interpolation was proposed for a small amount of uniformly sampling data points on 3-D surface.Firstly,we obtained the same amount of data points with to be reconstructed by using cubic B-spline interpolation method.Then,we adopted the discrete cosine transform(DCT)for the 3-D coordinate sparse representation respectively,and the designed uncertainty measurement matrix for signal observation.Finally,we used Weak choose Regularized Orthogonal Matching Pursuit(WROMP)as reconstruction algorithm.Experimental results show that the tissue surface reconstruction data error based on the method proposed is small,and the reconstructed surface is smooth,which can provide accurate surface data model for surgery simulation system based on VR.