Just Flip: Flipped Observation Generation and Optimization for Neural Radiance Fields to Cover Unobserved View

With the advent of Neural Radiance Field (NeRF), representing 3D scenes through multiple observations has shown significant improvements. Since this cutting-edge technique can 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 absent observation for unexplored robot trajectory. Our approach involves a data augmentation technique for 3D reconstruction using NeRF, by flipping observed images and estimating the 6DOF poses of the flipped cameras. Furthermore, to ensure the NeRF model operates robustly in general scenarios, we also propose a training method that adjusts the flipped pose and considers the uncertainty in flipped images accordingly. Our technique does not utilize an additional network, making it simple and fast, thus ensuring its suitability for robotic applications where real-time performance is crucial.