View Synthesis for Dynamic Scenes, With and Without Reconstruction

本研究探讨新颖观点的合成问题 的 动态的三维场景，包含多个运动中的刚性物体，给定一组捕获的照片 在 不同位置和不同时间。开发了基于图像的内插和外插方法，以便可以合成物理上正确的运动序列，从而显示场景对象在 的 失踪 时间 interval. 对应于对象可以经历的刚性运动的基本类型的运动类别（例如， 平移运动或绕单个轴的旋转）。 对于每一个类， 物理上正确 查看 没有 首先恢复场景的几何形状，将被设计、实现和测试。 作为基于非重构图像观察和运动合成的替代，还将探索利用对象运动信息来执行相机自校准和3D场景重构的方法。 通过恢复消失点和基本矩阵等性质， 每个 移动 本发明目的， 相机 可以直接计算校准信息，而不需要点对应。 结果将包括 对动态场景的基于图像的方法的更好的理论理解，以及用于运动插值和多视图、基于运动的场景重建的新算法。

This research investigates the problem of synthesizing novel views of dynamic, three-dimensional scenes containing multiple rigid objects in motion, given a set of photographs captured at different positions and different times. Image-based interpolation and extrapolation methods are developed so that physically-correct motion sequences can be synthesized showing how the scene objects could have moved during the missing time interval. Motion classes corresponding to basic types of rigid motion an object can undergo (e.g., translational motion or rotation about a single axis) will be used. For each class, techniques for synthesizing physically-correct views without first recovering the scene geometry, will be designed, implemented and tested. As an alternative to non-reconstructive image-based view and motion synthesis, methods that utilize object motion information to perform camera self-calibration and 3D scene reconstruction will also be explored. By recovering properties such as the vanishing point and the fundamental matrix for each moving object, camera calibration information can be directly computed without necessitating point correspondences. Results will include a better theoretical understanding of image-based methods for dynamic scenes, and new algorithms for motion interpolation and multi-view, motion-based scene reconstruction.