Unifying Correspondence, Pose and NeRF for Pose-Free Novel View Synthesis from Stereo Pairs

• URL: http://arxiv.org/abs/2312.07246v2
• Date: Mon, 8 Apr 2024 07:07:02 GMT
• Title: Unifying Correspondence, Pose and NeRF for Pose-Free Novel View Synthesis from Stereo Pairs
• Authors: Sunghwan Hong, Jaewoo Jung, Heeseong Shin, Jiaolong Yang, Seungryong Kim, Chong Luo,
• Abstract summary: 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.
• Score: 57.492124844326206
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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