This work introduces a novel augmentation method that increases the diversity of a train set to improve the generalization abilities of a 6D pose estimation network. For this purpose, a Neural Radiance Field is trained from synthetic images and exploited to generate an augmented set. Our method enriches the initial set by enabling the synthesis of images with (i) unseen viewpoints, (ii) rich illumination conditions through appearance extrapolation, and (iii) randomized textures. We validate our augmentation method on the challenging use-case of spacecraft pose estimation and show that it significantly improves the pose estimation generalization capabilities. On the SPEED+ dataset, our method reduces the error on the pose by 50% on both target domains.

翻译：本文提出了一种新颖的数据增强方法，通过提升训练集的多样性来增强六维位姿估计网络的泛化能力。该方法首先利用合成图像训练神经辐射场，进而生成增强数据集。我们的方法通过以下方式丰富初始数据集：(i) 合成具有未见视角的图像，(ii) 通过外观外推生成丰富光照条件下的图像，以及(iii) 生成随机纹理的图像。我们在极具挑战性的航天器位姿估计任务上验证了本增强方法的有效性，结果表明该方法显著提升了位姿估计的泛化性能。在SPEED+数据集上，我们的方法将两个目标域的位姿估计误差降低了50%。