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.

電腦視覺三維建模的精確度，仰賴影像中對應點的準確性。以前的研究大多採取兩張影像，透過極線轉換(epipolar transfer)取得影像間基礎矩陣(fundamental matrix)的關係，然後進行比對或過濾不良的對應點以求取精確的對應點。然極線轉換存在退化的問題，如何避免此退化問題以及降低兩張影像之間轉換錯誤的累積，成為求取精確三維建模中極待解決的課題。 本論文中，我們提出一套機制，透過三焦張量(trifocal tensor)的觀念來過濾影像間不良的對應點，提高整體對應點的準確度，從而能計算較精確的投影矩陣進行三維建模。我們由多視角影像出發，先透過Bundler求取對應點，然後採用三焦張量過濾Bundler產生的對應點，並輔以最小中值平方法(LMedS)提升選點之準確率，再透過權重以及重複過濾等機制來調節並過濾對應點，從而取得精確度較高的對應點組合，最後求取投影矩陣進行電腦視覺中的各項應用。 實作中，我們測詴了三組資料，包含一組以3ds Max自行建置的資料與兩組網路中取得的資料。我們先從三張影像驗證三焦張量的幾何特性與其過濾對應點的可行性，再將此方法延伸至多張影像，同樣也能證實透過三焦張量確實能提升對應點的準確度，甚至可以過濾出輸入資料中較不符合彼此間幾何性的影像。 / The accuracy of 3D model constructions in computer vision depends on the accuracy of the corresponding points extracted from the images. Previous studies in this area mostly use two images and compute the fundamental matrix through the use of the epipolar geometry and then proceed for corresponding point matching and filtering out the outliers in order to get accurate corresponding points. However, the epipoler transform suffers from the degenerate problems and, also, the accumulated conversion errors during the corresponding matches both will degrade the model accuracy. Solving these problems become crucial in reconstructing accurate 3D models from multiple images. In this thesis, we proposed a mechanism to obtain accurate corresponding points for 3D model reconstruction from multiple images. The concept of trifocal tensor is used to remove the outliers in order to improve the overall accuracy of the corresponding points. We first use Bundler to search the corresponding points in the feature points extracted from multiple view images. Then we use trifocal tensor to determine and remove the outliers in the corresponding points generated by Bundler. LMedS is used in these processes to improve the accuracy of the selected points. One can also improve the accuracy of the corresponding points through the use of weighting function as well as repeated filtering mechanism. With these high precision corresponding points, we can compute more accurate fundamental matrix in order to reconstruct the 3D models and other applications in computer vision. We have tested three sets of data, one of that is self-constructed data using the 3ds Max and the other two are downloaded from the internet. We started by demonstrating the geometric properties of trifocal tensor associated with three images and showed that it can be used to filter out the bad corresponding points. Then, we successfully extended this mechanism to more images and successfully improved the accuracy of the corresponding points among these images.

Virtual reality systems are nowadays common part of many research institutes due to its low cost and effective visualization of data. They mostly allow visualization and exploration of virtual worlds, but many lack user interaction. In this paper we suggest multi-camera optical system, which allows effective user interaction, thereby increasing immersion of virtual system. This paper describes the calibration process of multiple cameras using point correspondences.

No description available.

The Master thesis is focused on analyzing stereo camera ZED in the outdoor environment. There is compared ZEDfu visual odometry with commonly used methods like GPS or wheel odometry. Moreover, the thesis includes analyses of SLAM in the changeable outdoor environment, too. The simultaneous mapping and localization in RTAB-Map were processed separately with SIFT and BRISK descriptors. The aim of this master thesis is to analyze the behaviour ZED camera in the outdoor environment for future implementation in mobile robotics.

This thesis deals with reconstruction of golf ball position using multiple cameras. Reconstruction will be used for golf simulator project. System is using fotogrametric calibration and triangulation algorithm for obtaing point coordinates. Work also discuss options for camera selection. The result is making of prototype of the simulator.