With the advent of Neural Radiance Field (NeRF), representing 3D scenes through multiple observations has shown remarkable improvements in performance. Since this cutting-edge technique is able to obtain high-resolution renderings by interpolating dense 3D environments, various approaches have been proposed to apply NeRF for the spatial understanding of robot perception. However, previous works are challenging to represent unobserved scenes or views on the unexplored robot trajectory, as these works do not take into account 3D reconstruction without observation information. To overcome this problem, we propose a method to generate flipped observation in order to cover unexisting observation for unexplored robot trajectory. To achieve this, we propose a data augmentation method for 3D reconstruction using NeRF by flipping observed images, and estimating flipped camera 6DOF poses. Our technique exploits the property of objects being geometrically symmetric, making it simple but fast and powerful, thereby making it suitable for robotic applications where real-time performance is important. We demonstrate that our method significantly improves three representative perceptual quality measures on the NeRF synthetic dataset.
翻译:随着通过多次观测代表3D场的Neoral Radiance Field(NeRF)的出现,通过多种观测代表了3D场的景象,其性能显示出显著的改善。由于这一尖端技术能够通过内插密集的 3D 环境获得高分辨率的显示,因此建议了各种方法来应用NeRF对机器人感知的空间理解。然而,以往的工程很难在未探索的机器人轨迹上代表未观测到的场景或观点,因为这些工程没有在没有观察信息的情况下考虑3D重建。为了克服这一问题,我们提出了一种生成翻转式观测的方法,以覆盖未探索的机器人轨迹的未存在的观测。为了实现这一目标,我们提议了一种数据增强方法,用NeRF翻转观察到的图像来进行3D重建,并估计翻动的相机 6DOF 构成。我们的技术利用了几何对称对称对称的物体的特性,使其简单而快速和强大,从而使其适合实时性能很重要的机器人应用。我们的方法大大改进了NRF合成数据集上三种有代表性的质量措施。我们的方法。我们的方法大大改进了三种有代表性的方法。</s>