We present Omnidirectional Neural Radiance Fields (OmniNeRF), the first method to the application of parallax-enabled novel panoramic view synthesis. Recent works for novel view synthesis focus on perspective images with limited field-of-view and require sufficient pictures captured in a specific condition. Conversely, OmniNeRF can generate panorama images for unknown viewpoints given a single equirectangular image as training data. To this end, we propose to augment the single RGB-D panorama by projecting back and forth between a 3D world and different 2D panoramic coordinates at different virtual camera positions. By doing so, we are able to optimize an Omnidirectional Neural Radiance Field with visible pixels collecting from omnidirectional viewing angles at a fixed center for the estimation of new viewing angles from varying camera positions. As a result, the proposed OmniNeRF achieves convincing renderings of novel panoramic views that exhibit the parallax effect. We showcase the effectiveness of each of our proposals on both synthetic and real-world datasets.

翻译：我们提出OmniNerial 神经光谱场(OmniNerais Fields),这是应用3D世界和不同2D全景座标在不同虚拟相机位置上进行反射的首个方法。通过这样做,我们能够优化全向神经神经光谱场,在固定中心收集可见的像素,用于估计不同摄像位置的新角度。因此,拟议的OmniNeRF能够令人信服地展示展示显示普罗纳克斯效应的新全景观点。我们展示了我们在合成和现实世界数据集上提出的每一项建议的有效性。