LiDAR mapping is important yet challenging in self-driving and mobile
robotics. To tackle such a global point cloud registration problem, DeepMapping
converts the complex map estimation into a self-supervised training of simple
deep networks. Despite its broad convergence range on small datasets,
DeepMapping still cannot produce satisfactory results on large-scale datasets
with thousands of frames. This is due to the lack of loop closures and exact
cross-frame point correspondences, and the slow convergence of its global
localization network. We propose DeepMapping2 by adding two novel techniques to
address these issues: (1) organization of training batch based on map topology
from loop closing, and (2) self-supervised local-to-global point consistency
loss leveraging pairwise registration. Our experiments and ablation studies on
public datasets (KITTI, NCLT, and Nebula) demonstrate the effectiveness of our
method.

DeepMapping2 提出了两种新技术，基于地图拓扑的组织训练数据批次，以及借助成对的点云配准进行自监督局部 - 全局点云一致性损失，解决 DeepMapping 在大规模数据集中计算慢、全局定位网络收敛不足和缺少闭环和精确跨帧点对应等问题，实验表明了我们方法的有效性。

DeepMapping2: 自监督大规模 LiDAR 地图优化

DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization

We introduce a technique for pairwise registration of neural fields that
extends classical optimization-based local registration (i.e. ICP) to operate
on Neural Radiance Fields (NeRF) -- neural 3D scene representations trained
from collections of calibrated images. NeRF does not decompose illumination and
color, so to make registration invariant to illumination, we introduce the
concept of a ''surface field'' -- a field distilled from a pre-trained NeRF
model that measures the likelihood of a point being on the surface of an
object. We then cast nerf2nerf registration as a robust optimization that
iteratively seeks a rigid transformation that aligns the surface fields of the
two scenes. We evaluate the effectiveness of our technique by introducing a
dataset of pre-trained NeRF scenes -- our synthetic scenes enable quantitative
evaluations and comparisons to classical registration techniques, while our
real scenes demonstrate the validity of our technique in real-world scenarios.
Additional results available at: this https URL

介绍了一种基于神经辐射场的配对注册技术，通过引入表面场的概念与鲁棒优化的算法来实现 NeRF 场景的配准，该技术可以应用于不同领域的任务。

神经辐射场的配对对齐

nerf2nerf: Pairwise Registration of Neural Radiance Fields

We introduce PREDATOR, a model for pairwise point-cloud registration with
deep attention to the overlap region. Different from previous work, our model
is specifically designed to handle (also) point-cloud pairs with low overlap.
Its key novelty is an overlap-attention block for early information exchange
between the latent encodings of the two point clouds. In this way the
subsequent decoding of the latent representations into per-point features is
conditioned on the respective other point cloud, and thus can predict which
points are not only salient, but also lie in the overlap region between the two
point clouds. The ability to focus on points that are relevant for matching
greatly improves performance: PREDATOR raises the rate of successful
registrations by more than 20% in the low-overlap scenario, and also sets a new
state of the art for the 3DMatch benchmark with 89% registration recall.

我们引入了 PREDATOR 模型，这是一种针对点云配对注册的模型，通过深度注意重叠区域来实现，其重要特点是重叠注意力块用于两个点云的潜在编码之间的早期信息交换，从而可以预测哪些点不仅很重要，而且位于两个点云之间的重叠区域，大大提高了匹配的准确性，这项工作在低重叠情况下成功匹配率提高了 20％以上，在 3DMatch benchmark 上也创造了新的纪录，注册召回率为 89％。

PREDATOR: 低重叠 3D 点云注册

PREDATOR: Registration of 3D Point Clouds with Low Overlap

We present Deep Global Registration, a differentiable framework for pairwise
registration of real-world 3D scans. Deep global registration is based on three
modules: a 6-dimensional convolutional network for correspondence confidence
prediction, a differentiable Weighted Procrustes algorithm for closed-form pose
estimation, and a robust gradient-based SE(3) optimizer for pose refinement.
Experiments demonstrate that our approach outperforms state-of-the-art methods,
both learning-based and classical, on real-world data.

我们提出了一个基于神经网络和优化算法的 3D 扫描配准框架，可以对实际数据进行高精度的配准，优于现有的同类方法。