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.

翻译：在机器人导航和远程视觉辅助等各类应用中，扩展相机的视场已被证明有助于增强环境感知能力。与仅旨在生成美观视觉效果的传统图像外推绘制技术不同，这些应用要求扩展的视图能够高保真地还原真实场景。为实现这一目标，我们定义了一个新的高保真视场外推问题，该问题利用预先采集的一组图像作为场景先验知识。为解决该问题，我们提出了一种简洁而有效的解决方案——NeRF增强的外推绘制方法，该方法通过NeRF生成的扩展视场图像来训练场景特定的图像外推绘制模型。为评估NEO的性能，我们在三个照片级真实感数据集和一个真实世界数据集上进行了全面评测。在基准数据集上的大量实验结果表明，该方法在应对这一挑战时展现出鲁棒性和潜力。我们相信，这项研究为学术界的未来探索奠定了坚实基础。