Unifying Correspondence Pose and NeRF for Generalized Pose-Free Novel View Synthesis

Abstract

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.