场景坐标重建：通过重新定位器的增量学习对图像集合进行姿势建模

摘要

我们致力于从描绘场景的图像集中估计摄像机参数的任务。流行的基于特征的运动结构（SfM）工具通过增量重建来解决这一任务：它们重复对稀疏的3D点进行三角测量，并将更多摄像机视图注册到稀疏点云中。我们重新解释增量式运动结构为对视觉重定位器的迭代应用和细化，即一种将新视图注册到重建当前状态的方法。这种视角使我们能够研究不基于局部特征匹配的替代视觉重定位器。我们展示了一种称为场景坐标回归的基于学习的重定位方法，它使我们能够从未定位的图像中构建隐式的神经场景表示。与其他基于学习的重建方法不同，我们不需要姿势先验或顺序输入，并且我们可以高效地优化数千幅图像。我们的方法ACE0（ACE Zero）通过新颖的视图合成展示了与基于特征的SfM相当的摄像机姿势估计精度。项目页面：https://nianticlabs.github.io/acezero/

English

We address the task of estimating camera parameters from a set of images depicting a scene. Popular feature-based structure-from-motion (SfM) tools solve this task by incremental reconstruction: they repeat triangulation of sparse 3D points and registration of more camera views to the sparse point cloud. We re-interpret incremental structure-from-motion as an iterated application and refinement of a visual relocalizer, that is, of a method that registers new views to the current state of the reconstruction. This perspective allows us to investigate alternative visual relocalizers that are not rooted in local feature matching. We show that scene coordinate regression, a learning-based relocalization approach, allows us to build implicit, neural scene representations from unposed images. Different from other learning-based reconstruction methods, we do not require pose priors nor sequential inputs, and we optimize efficiently over thousands of images. Our method, ACE0 (ACE Zero), estimates camera poses to an accuracy comparable to feature-based SfM, as demonstrated by novel view synthesis. Project page: https://nianticlabs.github.io/acezero/

场景坐标重建：通过重新定位器的增量学习对图像集合进行姿势建模

Scene Coordinate Reconstruction: Posing of Image Collections via Incremental Learning of a Relocalizer

摘要

Summary

Support

Support