We present iNeRF, a framework that performs mesh-free pose estimation by
"inverting" a Neural RadianceField (NeRF). NeRFs have been shown to be
remarkably effective for the task of view synthesis - synthesizing
photorealistic novel views of real-world scenes or objects. In this work, we
investigate whether we can apply analysis-by-synthesis via NeRF for mesh-free,
RGB-only 6DoF pose estimation - given an image, find the translation and
rotation of a camera relative to a 3D object or scene. Our method assumes that
no object mesh models are available during either training or test time.
Starting from an initial pose estimate, we use gradient descent to minimize the
residual between pixels rendered from a NeRF and pixels in an observed image.
In our experiments, we first study 1) how to sample rays during pose refinement
for iNeRF to collect informative gradients and 2) how different batch sizes of
rays affect iNeRF on a synthetic dataset. We then show that for complex
real-world scenes from the LLFF dataset, iNeRF can improve NeRF by estimating
the camera poses of novel images and using these images as additional training
data for NeRF. Finally, we show iNeRF can perform category-level object pose
estimation, including object instances not seen during training, with RGB
images by inverting a NeRF model inferred from a single view.

iNeRF 是一种基于 “倒置” 神经辐射场（NeRF）的框架，它可以通过合成图像与观察图像像素之间的残差最小化来实现无网格姿态估计。实验表明，iNeRF 可以应用于复杂的真实场景中，还可以执行目录级别物体姿态估计。