  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.
