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自動化三維建模需要準確的三維點座標，而三維點的位置則依賴高精度的對應點，因此對應點的尋找一直是此領域的研究議題，而使用稀疏光束調整法(SBA：Sparse Bundle Adjustment)來優化相機參數也是常用的作法，然而若三維點當中有少數幾個誤差較大的點，則稀疏光束調整法會受到很大的影響。我們採用多視角影像做依據，找出對應點座標及幾何關係，在改善對應點位置的步驟中，我們藉由位移三維點法向量來取得各種不同位置的三維補綴面(3D patch)，並根據投影到影像上之補綴面的正規化相關匹配法(NCC：Normalized Cross Correlation)來改善對應點位置。利用這些改善過的點資訊，我們使用稀疏光束調整法來針對相機校正做進一步的優化，為了避免誤差較大的三維點影響到稀疏光束調整法的結果，我們使用穩健的計算方法來過濾這些三維點，藉由此方法來減少再投影誤差(reprojection error)，最後產生較精準的相機參數，使用此參數我們可以自動化建出外型架構較接近真實物體的模型。 / Automated 3D modeling of the need for accurate 3D points, and location of the 3D points depends on the accuracy of corresponding points, so the search for corresponding points in this area has been a research topic, and the use of SBA(Sparse Bundle Adjustment) to optimize the camera parameters is also a common practice, however, if there are a few more error 3D points, the SBA will be greatly affected. In this paper, we establish the corresponding points and their geometry relationship from multi-view images. And the 3D patches are used to refine point positions. We translate the normal to get many patches, and project them into visible images. The NCC(Normalized Cross Correlation) values between patches in reference image and patches in visible image are used to estimate the best correspondence points. And they are used to get better camera parameters by SBA(sparse bundle adjustment). Furthermore, it is because that it usually exist outliers in the data observed, and they will influence the results by using SBA. So, we use our robust estimation method to resist the outliers. In our experiment, SBA is used to filter some outliers to reduce the reprojection error. After getting more precise camera parameters, we use them to reconstruct the 3D model more realistic.