Neural radiance fields enable novel-view synthesis and scene reconstruction with photorealistic quality from a few images, but require known and accurate camera poses. Conventional pose estimation algorithms fail on smooth or self-similar scenes, while methods performing inverse rendering from unposed views require a rough initialization of the camera orientations. The main difficulty of pose estimation lies in real-life objects being almost invariant under certain transformations, making the photometric distance between rendered views non-convex with respect to the camera parameters. Using an equivalence relation that matches the distribution of local minima in camera space, we reduce this space to its quotient set, in which pose estimation becomes a more convex problem. Using a neural-network to regularize pose estimation, we demonstrate that our method - MELON - can reconstruct a neural radiance field from unposed images with state-of-the-art accuracy while requiring ten times fewer views than adversarial approaches.

翻译：摘要：神经辐射场能够从少量图像中实现具有照片级真实感的新视角合成与场景重建，但需要已知且准确的相机位姿。传统的位姿估计算法在光滑或自相似场景中会失效，而基于无位姿视角进行逆渲染的方法则需要对相机朝向进行粗略初始化。位姿估计的主要困难在于现实物体在特定变换下几乎具有不变性，这使得渲染视角间的光度距离相对于相机参数呈非凸性。通过利用与相机空间中局部最小值分布相匹配的等价关系，我们将该空间约简至其商集，在此集合中位姿估计转化为更凸性的问题。借助神经网络对位姿估计进行正则化，我们证明所提出的MELON方法能够从无位姿图像中重建神经辐射场，并以最先进的精度实现这一目标，同时所需视角数量比对抗性方法少十倍。