Neural surface reconstruction has been shown to be powerful for recovering
dense 3D surfaces via image-based neural rendering. However, current methods
struggle to recover detailed structures of real-world scenes. To address the
issue, we present Neuralangelo, which combines the representation power of
multi-resolution 3D hash grids with neural surface rendering. Two key
ingredients enable our approach: (1) numerical gradients for computing
higher-order derivatives as a smoothing operation and (2) coarse-to-fine
optimization on the hash grids controlling different levels of details. Even
without auxiliary inputs such as depth, Neuralangelo can effectively recover
dense 3D surface structures from multi-view images with fidelity significantly
surpassing previous methods, enabling detailed large-scale scene reconstruction
from RGB video captures.

本文介绍了一种新的神经表面重建方法 Neuralangelo，该方法结合了多分辨率 3D 哈希网格和神经表面渲染的表示能力。通过利用数值梯度和粗到细优化控制不同级别的细节，即使在没有深度等辅助输入的情况下，Neuralangelo 也可以从多视角图像中有效地恢复密集的 3D 表面结构，比以前的方法实现了更高的精度，从而使得可以用 RGB 视频捕获对大规模场景进行详细的重建。

神经安格洛：高保真神经表面重建

Neuralangelo: High-Fidelity Neural Surface Reconstruction

Learning-based point cloud registration methods can handle clean point clouds
well, while it is still challenging to generalize to noisy and partial point
clouds. To this end, we propose a novel framework for noisy and partial point
cloud registration. By introducing a neural implicit function representation,
we replace the problem of rigid registration between point clouds with a
registration problem between the point cloud and the neural implicit function.
We then alternately optimize the implicit function representation and the
registration between the implicit function and point cloud. In this way, point
cloud registration can be performed in a coarse-to-fine manner. Since our
method avoids computing point correspondences, it is robust to the noise and
incompleteness of point clouds. Compared with the registration methods based on
global features, our method can deal with surfaces with large density
variations and achieve higher registration accuracy. Experimental results and
comparisons demonstrate the effectiveness of the proposed framework.

我们提出了一个新的基于神经隐式函数表达的框架，用于处理噪声和不完整点云的注册问题，并通过一个分阶段的优化方式，实现了点云的精确匹配，避免了点对应计算，提升了鲁棒性和精度。

SDFReg: 学习点云配准的有符号距离函数

SDFReg: Learning Signed Distance Functions for Point Cloud Registration

Absolute camera pose estimation is usually addressed by sequentially solving
two distinct subproblems: First a feature matching problem that seeks to
establish putative 2D-3D correspondences, and then a Perspective-n-Point
problem that minimizes, with respect to the camera pose, the sum of so-called
Reprojection Errors (RE). We argue that generating putative 2D-3D
correspondences 1) leads to an important loss of information that needs to be
compensated as far as possible, within RE, through the choice of a robust loss
and the tuning of its hyperparameters and 2) may lead to an RE that conveys
erroneous data to the pose estimator. In this paper, we introduce the Neural
Reprojection Error (NRE) as a substitute for RE. NRE allows to rethink the
camera pose estimation problem by merging it with the feature learning problem,
hence leveraging richer information than 2D-3D correspondences and eliminating
the need for choosing a robust loss and its hyperparameters. Thus NRE can be
used as training loss to learn image descriptors tailored for pose estimation.
We also propose a coarse-to-fine optimization method able to very efficiently
minimize a sum of NRE terms with respect to the camera pose. We experimentally
demonstrate that NRE is a good substitute for RE as it significantly improves
both the robustness and the accuracy of the camera pose estimate while being
computationally and memory highly efficient. From a broader point of view, we
believe this new way of merging deep learning and 3D geometry may be useful in
other computer vision applications.

本文提出了神经投影误差作为重合问题与特征学习问题的结合，使用粗 - 细优化方法极大地提高了定位精度和鲁棒性。