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（Neural Radiance Field, NeRF）凭借其优异的渲染图像质量和简洁的架构，已成为新视角合成领域的主流方法。尽管NeRF在多个方向上持续提升性能，但密集多视角图像集的依赖仍然制约着其实际应用的发展。本文提出FlipNeRF——一种利用翻转反射射线的少样本新视角合成正则化方法。翻转反射射线由输入射线方向与估计法向量显式推导得出，既可充当有效的额外训练射线，又能更精确地估计表面法向量并有效学习三维几何结构。由于表面法向量与场景深度均沿射线从估计密度导出，精确的表面法向量可带来更准确的深度估计，而这正是少样本新视角合成的关键要素。同时，通过提出的不确定性感知空域损失（Uncertainty-aware Emptiness Loss）与瓶颈特征一致性损失（Bottleneck Feature Consistency Loss），FlipNeRF能够在不同场景结构中有效减少漂浮伪影，输出更可靠的结果；并且无需额外特征提取器，即可增强投射至光度一致像素的射线对间的特征级一致性。我们的FlipNeRF在多个基准测试的所有场景中均实现了最先进的性能。