Just Flip：用于覆盖未观测视角的神经辐射场的翻转观测生成与优化
Just Flip: Flipped Observation Generation and Optimization for Neural Radiance Fields to Cover Unobserved View
AI总结：
针对NeRF在未探索机器人轨迹上无法表示未观测视角的问题，提出通过翻转观测图像并估计相机6DOF位姿的数据增强方法，利用物体几何对称性提升3D重建的实时性与感知质量。
摘要：
随着Neural Radiance Field (NeRF)的出现，通过多个观测表示3D场景在性能上展现出显著提升。由于这项前沿技术能够通过插值密集3D环境获得高分辨率渲染，人们提出了多种将NeRF应用于机器人感知空间理解的方法。然而，以往的工作难以表示未探索机器人轨迹上的未观测场景或视角，因为这些工作未考虑在缺乏观测信息的情况下进行3D重建。为克服这一问题，我们提出了一种生成翻转观测的方法，以弥补未探索机器人轨迹上缺失的观测。为此，我们提出了一种基于NeRF的3D重建数据增强方法，通过翻转观测图像并估计翻转的相机6DOF位姿来实现。我们的技术利用了物体几何对称的特性，使其简单但快速且强大，从而适用于对实时性要求较高的机器人应用。我们证明了我们的方法在NeRF合成数据集上显著改善了三种代表性的感知质量指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
