This work delves into the task of pose-free novel view synthesis from stereo
pairs, a challenging and pioneering task in 3D vision. Our innovative
framework, unlike any before, seamlessly integrates 2D correspondence matching,
camera pose estimation, and NeRF rendering, fostering a synergistic enhancement
of these tasks. We achieve this through designing an architecture that utilizes
a shared representation, which serves as a foundation for enhanced 3D geometry
understanding. Capitalizing on the inherent interplay between the tasks, our
unified framework is trained end-to-end with the proposed training strategy to
improve overall model accuracy. Through extensive evaluations across diverse
indoor and outdoor scenes from two real-world datasets, we demonstrate that our
approach achieves substantial improvement over previous methodologies,
especially in scenarios characterized by extreme viewpoint changes and the
absence of accurate camera poses.

这项工作涉及从立体对生成无姿态新视角合成的任务，在 3D 视觉中是一个具有挑战性和开创性的任务。通过设计一个利用共享表示的架构，无缝集成 2D 对应匹配、相机姿态估计和 NeRF 渲染，促进这些任务的协同增强，我们在整个模型准确性上通过提议的训练策略进行端到端的培训。通过对两个现实世界数据集的多样化室内和室外场景进行广泛评估，我们证明我们的方法在特征视角变化和缺乏精确相机姿态的场景中实现了相当大的改进。