  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.
