  We present DPPE, a dense pose estimation algorithm that functions over a
Plenoxels environment. Recent advances in neural radiance field techniques have
shown that it is a powerful tool for environment representation. More recent
neural rendering algorithms have significantly improved both training duration
and rendering speed. Plenoxels introduced a fully-differentiable radiance field
technique that uses Plenoptic volume elements contained in voxels for
rendering, offering reduced training times and better rendering accuracy, while
also eliminating the neural net component. In this work, we introduce a 6-DoF
monocular RGB-only pose estimation procedure for Plenoxels, which seeks to
recover the ground truth camera pose after a perturbation. We employ a
variation on classical template matching techniques, using stochastic gradient
descent to optimize the pose by minimizing errors in re-rendering. In
particular, we examine an approach that takes advantage of the rapid rendering
speed of Plenoxels to numerically approximate part of the pose gradient, using
a central differencing technique. We show that such methods are effective in
pose estimation. Finally, we perform ablations over key components of the
problem space, with a particular focus on image subsampling and Plenoxel grid
resolution. Project website: https://sites.google.com/view/dppe
