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

翻译：我们提出DPPE，一种在普朗光场（Plenoxels）环境中运行的密集姿态估计算法。神经辐射场技术的最新进展表明，其在环境表征方面具有强大能力。更近期的神经渲染算法显著提升了训练时长和渲染速度。Plenoxels引入了一种全可微辐射场技术，利用包含在体素中的全光体积元素进行渲染，在降低训练时间的同时提高渲染精度，并且消除了神经网络组件。本文提出了一种适用于Plenoxels的6自由度单目纯RGB姿态估计方法，旨在从扰动中恢复真实相机姿态。我们采用经典模板匹配技术的变体，利用随机梯度下降通过最小化重渲染误差来优化姿态。特别地，我们研究了一种利用Plenoxels快速渲染速度的方法，通过中心差分技术数值近似部分姿态梯度。实验表明，此类方法在姿态估计中有效。最后，我们对问题空间的关键组件进行消融研究，重点关注图像子采样和Plenoxel网格分辨率。项目网站：https://sites.google.com/view/dppe