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：随着神经辐射场(NeRF)的出现，通过多视角观测表示三维场景在性能上取得了显著提升。由于这种前沿技术能够通过插值密集三维环境获得高分辨率渲染图像，研究者提出了多种方法将NeRF应用于机器人感知的空间理解任务。然而，现有方法在处理未探索机器人轨迹上未观测到的场景或视角时面临挑战，因为这些方法未考虑缺乏观测信息的三维重建问题。为解决这一难题，我们提出了一种生成翻转观测的方法，以覆盖未探索机器人轨迹上缺失的观测数据。具体而言，我们提出了一种基于NeRF的三维重建数据增强方法，通过翻转观测图像并估计翻转后的相机六自由度位姿来实现。本技术利用物体几何对称性特性，在保持简洁性的同时兼具快速性与强鲁棒性，因此特别适用于对实时性要求较高的机器人应用场景。实验表明，我们的方法在NeRF合成数据集上显著提升了三个代表性感知质量指标的性能。