Data augmentation for NeRF: a geometric consistent solution based on view morphing

NeRF aims to learn a continuous neural scene representation by using a finite set of input images taken from different viewpoints. The fewer the number of viewpoints, the higher the likelihood of overfitting on them. This paper mitigates such limitation by presenting a novel data augmentation approach to generate geometrically consistent image transitions between viewpoints using view morphing. View morphing is a highly versatile technique that does not requires any prior knowledge about the 3D scene because it is based on general principles of projective geometry. A key novelty of our method is to use the very same depths predicted by NeRF to generate the image transitions that are then added to NeRF training. We experimentally show that this procedure enables NeRF to improve the quality of its synthesised novel views in the case of datasets with few training viewpoints. We improve PSNR up to 1.8dB and 10.5dB when eight and four views are used for training, respectively. To the best of our knowledge, this is the first data augmentation strategy for NeRF that explicitly synthesises additional new input images to improve the model generalisation.