Despite the rapid development of Neural Radiance Field (NeRF), the necessity
of dense covers largely prohibits its wider applications. While several recent
works have attempted to address this issue, they either operate with sparse
views (yet still, a few of them) or on simple objects/scenes. In this work, we
consider a more ambitious task: training neural radiance field, over
realistically complex visual scenes, by "looking only once", i.e., using only a
single view. To attain this goal, we present a Single View NeRF (SinNeRF)
framework consisting of thoughtfully designed semantic and geometry
regularizations. Specifically, SinNeRF constructs a semi-supervised learning
process, where we introduce and propagate geometry pseudo labels and semantic
pseudo labels to guide the progressive training process. Extensive experiments
are conducted on complex scene benchmarks, including NeRF synthetic dataset,
Local Light Field Fusion dataset, and DTU dataset. We show that even without
pre-training on multi-view datasets, SinNeRF can yield photo-realistic
novel-view synthesis results. Under the single image setting, SinNeRF
significantly outperforms the current state-of-the-art NeRF baselines in all
cases. Project page: this https URL

本论文提出了单视角 NeRF 框架 (SinNeRF)，通过引入半监督的学习过程，利用几何标签和语义标签指导训练过程，成功实现了仅通过单个视图将神经辐射场训练到现实场景，即使在不进行多视图数据集预训练的情况下，SinNeRF 可产生逼真的新视图综合结果。