Neural Radiance Field (NeRF) has been a mainstream in novel view synthesis with its remarkable quality of rendered images and simple architecture. Although NeRF has been developed in various directions improving continuously its performance, the necessity of a dense set of multi-view images still exists as a stumbling block to progress for practical application. In this work, we propose FlipNeRF, a novel regularization method for few-shot novel view synthesis by utilizing our proposed flipped reflection rays. The flipped reflection rays are explicitly derived from the input ray directions and estimated normal vectors, and play a role of effective additional training rays while enabling to estimate more accurate surface normals and learn the 3D geometry effectively. Since the surface normal and the scene depth are both derived from the estimated densities along a ray, the accurate surface normal leads to more exact depth estimation, which is a key factor for few-shot novel view synthesis. Furthermore, with our proposed Uncertainty-aware Emptiness Loss and Bottleneck Feature Consistency Loss, FlipNeRF is able to estimate more reliable outputs with reducing floating artifacts effectively across the different scene structures, and enhance the feature-level consistency between the pair of the rays cast toward the photo-consistent pixels without any additional feature extractor, respectively. Our FlipNeRF achieves the SOTA performance on the multiple benchmarks across all the scenarios.

翻译：神经辐射场（NeRF）凭借其卓越的渲染图像质量和简洁架构，已成为新颖视角合成领域的主流方法。尽管NeRF在多个方向上持续改进性能，但密集多视角图像集的需求仍是其实际应用中的关键障碍。本文提出FlipNeRF——一种利用所提出的翻转反射光线实现小样本新颖视角合成的新颖正则化方法。翻转反射光线由输入光线方向和估计法向量显式推导得出，既作为高效附加训练光线发挥作用，又能同时实现更精确的表面法线估计与有效的三维几何学习。由于表面法线和场景深度均沿光线从估计密度推导，精确表面法线可带来更准确的深度估计——这正是小样本新颖视角合成的关键要素。此外，通过所提出的不确定性感知空域损失与瓶颈特征一致性损失，FlipNeRF能有效减少不同场景结构下的漂浮伪影以输出更可靠的结果，并在无需额外特征提取器的情况下，增强投射至光度一致像素的成对光线间的特征级一致性。FlipNeRF在所有场景下的多个基准测试中均实现了最先进的性能。