Multiple-view 3d Reconstruction Based On Trifocal Tensor

Multiple-view reconstruction is extracting 3D information from multiple views of the object, and intent to reconstruction the object. Recent years, academic world has attached attention to Multiple-view reconstruction. This technology is widely used in the field of robot manufacturing, surface inspection, medical imaging, surgical navigation, image registration, and so on. Multiple-view reconstruction is based on multiple views, and can extract useful information from at least three images. The process can be divided into projective reconstruction and metric correction.Currently, the projective reconstruction commonly uses singular value decomposition of coefficient matrix to compute the trifocal tensor. This method easily leads to geometric invalid solution as it takes data redundancy in. In the process of metric correction, traditional methods are limited in practical application as they need vanishing points, vanishing lines or the special movement of camera. In this thesis, our study and discuss focused on the above two problems.We computed the trifocal tensor based on minimum case with duality principles, and avoid using singular value decomposition of coefficient matrix. Thus, the reconstruction is geometric valid. Further more, we obtain the right characteristic matching points and accurate trifocal tensor with effective robust estimate.We researched the stratified reconstruction, computed the infinity plane from projective reconstruction by solving the Cheirality inequality, and obtained the internal parameters matrix of camera by infinite homography. With camera self-calibration, projective reconstruction can be transform to affine reconstruction, and finally achieve metric correction. Compared to traditional multiple-view reconstruction methods, our method obtained an accurate minimum case solution, can be used wider as we reconstructed the 3D object without vanishing points or lines.