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: this https URL

我们提出了 DPPE 算法，它是一个基于 Plenoxels 环境的密集姿态估计算法，并通过随机梯度下降来优化姿态估计，利用 Plenoxels 的快速渲染速度来近似计算姿态梯度。

DPPE：在 Plenoxels 环境中使用梯度近似进行密集姿态估计

DPPE: Dense Pose Estimation in a Plenoxels Environment using Gradient  Approximation

Reconstructing neural radiance fields with explicit volumetric
representations, demonstrated by Plenoxels, has shown remarkable advantages on
training and rendering efficiency, while grid-based representations typically
induce considerable overhead for storage and transmission. In this work, we
present a simple and effective framework for pursuing compact radiance fields
from the perspective of compression methodology. By exploiting intrinsic
properties exhibiting in grid models, a non-uniform compression stem is
developed to significantly reduce model complexity and a novel parameterized
module, named Neural Codebook, is introduced for better encoding high-frequency
details specific to per-scene models via a fast optimization. Our approach can
achieve over 40 $\times$ reduction on grid model storage with competitive
rendering quality. In addition, the method can achieve real-time rendering
speed with 180 fps, realizing significant advantage on storage cost compared to
real-time rendering methods.

本文提出了一种使用压缩方法从紧凑的角度追求较小体积的神经辐射场的简单而有效的框架，该方法利用网格模型固有的代表特性，开发了非一致压缩方法以显着减少模型复杂性，并引入了名为 “神经码书” 的新颖参数化模块，通过快速优化来更好地编码每个场景模型的高频细节，从而实现对网格模型存储量的超过 40 倍的减少，在具有竞争性的渲染质量和 180 fps 的实时渲染速度的同时，相对于实时渲染方法而言具有存储成本的显著优势。

具有神经码本的紧凑实时辐射场

Compact Real-time Radiance Fields with Neural Codebook

We introduce Plenoxels (plenoptic voxels), a system for photorealistic view
synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical
harmonics. This representation can be optimized from calibrated images via
gradient methods and regularization without any neural components. On standard,
benchmark tasks, Plenoxels are optimized two orders of magnitude faster than
Neural Radiance Fields with no loss in visual quality.

Plenoxels 是一种用于光线追踪的系统，将场景表示为一个稀疏的 3D 网格模型，并通过梯度方法和正则化优化，能够实现与神经辐射场相当的质量，却快 100 倍。