论文标题
VM-NERF：通过视图变形来解决NERF的稀疏性
VM-NeRF: Tackling Sparsity in NeRF with View Morphing
论文作者
论文摘要
NERF旨在通过使用从各种观点拍摄的有限输入图像来学习连续的神经场景表示。 NERF方法的一个众所周知的局限性是它们对数据的依赖：观点越少，过度拟合的可能性就越高。本文通过引入一种新颖的方法来解决此问题，以使用视图变形在观点之间生成几何一致的图像过渡。我们的VM-NERF方法不需要关于场景结构的先验知识，因为视图变形基于投射几何形状的基本原理。 VM-NERF在标准NERF方法的训练过程中紧密整合了此几何视图生成过程。值得注意的是，我们的方法显着改善了新型视图综合，尤其是当只有几个观点可用时。实验评估揭示了对处理NERF模型中稀疏观点的当前方法的一致改进。当培训分别使用八个和四个视图时，我们报告的PSNR最高为1.8dB和1.0dB。源代码：\ url {https://github.com/mbortolon97/vm-nerf}
NeRF aims to learn a continuous neural scene representation by using a finite set of input images taken from various viewpoints. A well-known limitation of NeRF methods is their reliance on data: the fewer the viewpoints, the higher the likelihood of overfitting. This paper addresses this issue by introducing a novel method to generate geometrically consistent image transitions between viewpoints using View Morphing. Our VM-NeRF approach requires no prior knowledge about the scene structure, as View Morphing is based on the fundamental principles of projective geometry. VM-NeRF tightly integrates this geometric view generation process during the training procedure of standard NeRF approaches. Notably, our method significantly improves novel view synthesis, particularly when only a few views are available. Experimental evaluation reveals consistent improvement over current methods that handle sparse viewpoints in NeRF models. We report an increase in PSNR of up to 1.8dB and 1.0dB when training uses eight and four views, respectively. Source code: \url{https://github.com/mbortolon97/VM-NeRF}
