In various applications, such as robotic navigation and remote visual
assistance, expanding the field of view (FOV) of the camera proves beneficial
for enhancing environmental perception. Unlike image outpainting techniques
aimed solely at generating aesthetically pleasing visuals, these applications
demand an extended view that faithfully represents the scene. To achieve this,
we formulate a new problem of faithful FOV extrapolation that utilizes a set of
pre-captured images as prior knowledge of the scene. To address this problem,
we present a simple yet effective solution called NeRF-Enhanced Outpainting
(NEO) that uses extended-FOV images generated through NeRF to train a
scene-specific image outpainting model. To assess the performance of NEO, we
conduct comprehensive evaluations on three photorealistic datasets and one
real-world dataset. Extensive experiments on the benchmark datasets showcase
the robustness and potential of our method in addressing this challenge. We
believe our work lays a strong foundation for future exploration within the
research community.

扩展摄像机的视场在应用如机器人导航和远程视觉辅助等领域中对于增强环境感知大有裨益。本文提出了一种名为 NeRF-Enhanced Outpainting（NEO）的简单而有效的解决方案，利用 NeRF 生成的扩展视场图像来训练场景特定的图像扩展模型，旨在解决忠实地扩充视场的问题。通过全面评估三个逼真数据集和一个真实世界数据集上 NEO 的性能，大量实验证明了我们方法在应对此挑战方面的鲁棒性和潜力，我们相信我们的工作为未来研究社区的进一步探索奠定了坚实的基础。