The capabilities of monocular depth estimation (MDE) models are limited by the availability of sufficient and diverse datasets. In the case of MDE models for autonomous driving, this issue is exacerbated by the linearity of the captured data trajectories. We propose a NeRF-based data augmentation pipeline to introduce synthetic data with more diverse viewing directions into training datasets and demonstrate the benefits of our approach to model performance and robustness. Our data augmentation pipeline, which we call "NeRFmentation", trains NeRFs on each scene in the dataset, filters out subpar NeRFs based on relevant metrics, and uses them to generate synthetic RGB-D images captured from new viewing directions. In this work, we apply our technique in conjunction with three state-of-the-art MDE architectures on the popular autonomous driving dataset KITTI, augmenting its training set of the Eigen split. We evaluate the resulting performance gain on the original test set, a separate popular driving set, and our own synthetic test set.

我们提出了一种基于NeRF的数据增强流程，用于训练数据集，并通过在数据集中引入更多不同视角的合成数据，展示了我们的方法对模型性能和稳健性的好处。我们将这种数据增强流程称为“NeRFmentation”，并将其与三个最先进的MDE架构结合在一起，在流行的自动驾驶数据集KITTI上增加其特征分割的训练集。我们评估了在原始测试集、另一个流行的驾驶数据集和我们自己的合成测试集上获得的性能增益。

NeRFmentation: 基于NeRF的单目深度估计增强