We aim to improve the Inverted Neural Radiance Fields (iNeRF) algorithm which defines the image pose estimation problem as a NeRF based iterative linear optimization. NeRFs are novel neural space representation models that can synthesize photorealistic novel views of real-world scenes or objects. Our contributions are as follows: we extend the localization optimization objective with a depth-based loss function, we introduce a multi-image based loss function where a sequence of images with known relative poses are used without increasing the computational complexity, we omit hierarchical sampling during volumetric rendering, meaning only the coarse model is used for pose estimation, and we how that by extending the sampling interval convergence can be achieved even or higher initial pose estimate errors. With the proposed modifications the convergence speed is significantly improved, and the basin of convergence is substantially extended.

翻译：我们旨在改进逆神经辐射场（iNeRF）算法，该算法将图像姿态估计问题定义为基于NeRF的迭代线性优化。NeRF是一种新型神经空间表示模型，能够合成真实场景或物体的逼真新视角图像。我们的贡献如下：利用深度损失函数扩展了定位优化目标；引入了基于多图像的损失函数，在无需增加计算复杂度的前提下，可利用已知相对位姿的图像序列；在体渲染过程中省略了分层采样，即仅使用粗模型进行姿态估计；并证明通过扩展采样间隔，即使初始姿态估计误差较大，也可实现收敛。所提改进显著提升了收敛速度，并大幅扩展了收敛域。