为了能鲁棒精确地从目标的双视图复原其三维视觉信息，基于随机采样最小冗余子集新概念，并利用数据正则化技术，开发了一个根据目标的双视图特征点对集合，能鲁棒精确地复原其三维视觉信息的新算法，由于该算法有如下优点：（1）随机采样大幅度减少了子集的采样次数，并能确保好子集被采样到；（2）被采样到的最小冗余子集中的冗余信息能有效地用于检验该子集的正当性和优劣程度；（3）数据正则化技术又可有效地克服由数据病态带来的计算不稳定性。因此，在强噪声、高出格点率的恶劣条件下，该算法仍能高精度地复原目标的三维视觉信息，实验结果例证了此结论。

In order to robustly and accurately restore the 3D vision information of an object from its two perspective views, by means of the new idea of randomly sampling the minimal redundant subset, by utilizing the data regularization technique, we develop a new algorithm, which can robustly and accurately recovers the 3D vision information of an object from its two perspective view data--the set of their feature point pairs. Random sampling can significantly reduce the sampling number of subset and make the good subset surely sampled. The redundant information contained in the minimal redundant subset can be efficiently used to check the validity and goodness of the sampled subset. The data regularization technique can greatly alleviate the numerical unstability generated from the ill posed property of the data. So, the algorithm is able to work well with high accuracy under very hard condition of heavy noise and high outlier rate. The experiments have demonstrated that the processed results are satisfactory.