基于广义相机的模板三维重建
Shape-from-Template with Generalised Camera
AI总结：
本文针对多相机观测下的模板三维重建问题，提出基于广义相机模型的多种SfT方法，利用多视图相互约束提高非刚性配准精度，并通过凸规划与迭代细化求解，在合成与真实数据上验证了有效性。
摘要：
本文提出了一种新方法，用于将三维形状非刚性地配准到由多个相机组成的相机群所观测到的二维关键点上。将三维形状非刚性地配准到观测到的二维关键点，即模板三维重建（Shape-from-Template, SfT），已经在单幅图像上得到了广泛研究，但联合利用多相机信息的SfT为扩展已知应用场景的范围开辟了新方向，例如医学成像中的三维形状配准以及手持相机的配准等。我们将这种多相机设置用广义相机模型来表示；因此，任何观测任意变形物体的透视相机或正交相机的集合都可以被配准。我们为此类SfT提出了多种方法：第一种方法中，对应的关键点位于空间中一个已知三维点出发的方向向量上；第二种方法中，对应的关键点位于空间中一个未知三维点出发的方向向量上，但该方向相对于某个局部参考坐标系具有已知朝向；第三种方法中，除了对应关系之外，被成像物体的轮廓也是已知的。这些方法共同构成了广义相机下SfT问题的第一组解决方案。基于广义相机的SfT背后的关键思想是，在利用变形物体多个视图之间相互约束所提供的额外信息的同时估计变形形状，从而提高重建精度。基于对应关系的方法通过凸规划求解，而基于轮廓的方法则是对凸解结果进行迭代细化。我们在大量合成数据和真实数据上验证了所提方法的准确性。
英文摘要：
This article presents a new method for non-rigidly registering a 3D shape to 2D keypoints observed by a constellation of multiple cameras. Non-rigid registration of a 3D shape to observed 2D keypoints, i.e., Shape-from-Template (SfT), has been widely studied using single images, but SfT with information from multiple-cameras jointly opens new directions for extending the scope of known use-cases such as 3D shape registration in medical imaging and registration from hand-held cameras, to name a few. We represent such multi-camera setup with the generalised camera model; therefore any collection of perspective or orthographic cameras observing any deforming object can be registered. We propose multiple approaches for such SfT: the first approach where the corresponded keypoints lie on a direction vector from a known 3D point in space, the second approach where the corresponded keypoints lie on a direction vector from an unknown 3D point in space but with known orientation w.r.t some local reference frame, and a third approach where, apart from correspondences, the silhouette of the imaged object is also known. Together, these form the first set of solutions to the SfT problem with generalised cameras. The key idea behind SfT with generalised camera is the improved reconstruction accuracy from estimating deformed shape while utilising the additional information from the mutual constraints between multiple views of a deformed object. The correspondence-based approaches are solved with convex programming while the silhouette-based approach is an iterative refinement of the results from the convex solutions. We demonstrate the accuracy of our proposed methods on many synthetic and real data
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
