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：[译文摘要] 随着神经辐射场（NeRF）技术的出现，通过多视角观测表示三维场景的性能已取得显著提升。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结果，学界已提出多种方法将NeRF应用于机器人感知的空间理解任务。然而，现有方法难以表征未探索机器人轨迹上的未观测场景或视角，因为这些方法未考虑缺乏观测信息的三维重建问题。为克服这一局限，我们提出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相机的六自由度位姿实现。该技术利用物体几何对称特性，具有简捷、快速且鲁棒的特点，特别适合需要实时性能的机器人应用场景。实验表明，在NeRF合成数据集上，我们的方法显著提升了三种代表性感知质量指标。