Dense image matching from multi-view images

在三維模型的建構上，對應點的選取和改善佔有相當重要的地位。對應點的準確性影響整個建模的成效。本論文中我們提出了新的方法，透過極線轉換法(epipolar transfer)在多視角影像中做可見影像過濾和對應點改善。首先，我們以Furukawa所提出的方法，建構三維補綴面並加以做旋轉和位移，或是單純在二維影像移動對應點兩種方式選取初始對應點。然後再以本研究所提出的極線轉換法找到適當位置的對應點。接下來我們將每個三維點的可見影像(visible image)再次透過極線轉換法去檢查可見影像上的對應點位置是否適當，利用門檻值將不合適的對應點過濾掉。進一步針對對應點位置的改善和篩選，期望透過極線幾何法來找到位置最準確的對應點位置。最後比較實驗成果，觀察到以本研究所提出的方法做改善後，對應點準確度提高近百分之十五。In the construction of three-dimensional models, the selection and refinement of the correspondences plays a very important rule. The accuracy of the correspondences affects modeling results. In this paper, we proposed a new approach, that is filtering the visible images and improving the corresponding points in multi-view images by epipolar transfer method. First of all, we use Furukawa proposed method to construct three-dimensional patches and making rotation and displacement, or simply move the corresponding points in two-dimensional images are two ways to select the initial corresponding points. And then to use epipolar transfer method in this study to find the appropriate location of the corresponding points. Next we will check the corresponding points on the each 3D point’s visible image again through the polar transformation method , and we use the threshold value to filter out the corresponding points. Further the location of the corresponding points for the improvement and screening, hoped that through the epipolar geometry method to find the most accurate corresponding points’ location. Experimental results are compared to observe the improvements that the method proposed in this study, the corresponding point accuracy by nearly 15 percent