The typical point cloud sampling methods used in state estimation for mobile
robots preserve a high level of point redundancy. The point redundancy slows
down the estimation pipeline and can make real-time estimation drift in
geometrically symmetrical and structureless environments. We propose a novel
point cloud sampling method that is capable of lowering the effects of
geometrical degeneracies by minimizing redundancy within the cloud. The
proposed method is an alternative to the commonly used sparsification methods
that normalize the density of points to comply with the constraints on the
real-time capabilities of a robot. In contrast to density normalization, our
method builds on the fact that linear and planar surfaces contain a high level
of redundancy propagated into iterative estimation pipelines. We define the
concept of gradient flow quantifying the surface underlying a point. We also
show that maximizing the entropy of the gradient flow minimizes point
redundancy for robot ego-motion estimation. We integrate the proposed method
into the point-based KISS-ICP and feature-based LOAM odometry pipelines and
evaluate it experimentally on KITTI, Hilti-Oxford, and custom datasets from
multirotor UAVs. The experiments show that the proposed sampling technique
outperforms state-of-the-art methods in well-conditioned as well as in
geometrically-degenerated settings, in both accuracy and speed.

提出了一种新的点云采样方法，通过降低点云中的冗余，减少在几何对称和无结构环境下实时估计漂移的影响。该方法在机器人的几何运动估计中最大化梯度流的熵，从而最小化点的冗余。实验结果表明，该采样技术在良好条件和几何退化环境下的准确度和速度优于现有方法。

RMS: 冗余最小化点云采样用于退化环境下的实时姿态估计

RMS: Redundancy-Minimizing Point Cloud Sampling for Real-Time Pose  Estimation in Degenerated Environments

3D scanning as a technique to digitize objects in reality and create their 3D
models, is used in many fields and areas. Though the quality of 3D scans
depends on the technical characteristics of the 3D scanner, the common drawback
is the smoothing of fine details, or the edges of an object. We introduce
SepicNet, a novel deep network for the detection and parametrization of sharp
edges in 3D shapes as primitive curves. To make the network end-to-end
trainable, we formulate the curve fitting in a differentiable manner. We
develop an adaptive point cloud sampling technique that captures the sharp
features better than uniform sampling. The experiments were conducted on a
newly introduced large-scale dataset of 50k 3D scans, where the sharp edge
annotations were extracted from their parametric CAD models, and demonstrate
significant improvement over state-of-the-art methods.

介绍了一种新的深度网络 SepicNet 用于检测和参数化 3D 物体的棱角，包括一种自适应点云采样技术，通过大规模数据集的实验表明该方法的边缘检测效果优于现有技术。

SepicNet：基于曲线参数推断的三维形状锐边恢复

SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D  Shapes

Point Cloud Sampling and Recovery (PCSR) is critical for massive real-time
point cloud collection and processing since raw data usually requires large
storage and computation. In this paper, we address a fundamental problem in
PCSR: How to downsample the dense point cloud with arbitrary scales while
preserving the local topology of discarding points in a case-agnostic manner
(i.e. without additional storage for point relationship)? We propose a novel
Locally Invertible Embedding for point cloud adaptive sampling and recovery
(PointLIE). Instead of learning to predict the underlying geometry details in a
seemingly plausible manner, PointLIE unifies point cloud sampling and
upsampling to one single framework through bi-directional learning.
Specifically, PointLIE recursively samples and adjusts neighboring points on
each scale. Then it encodes the neighboring offsets of sampled points to a
latent space and thus decouples the sampled points and the corresponding local
geometric relationship. Once the latent space is determined and that the deep
model is optimized, the recovery process could be conducted by passing the
recover-pleasing sampled points and a randomly-drawn embedding to the same
network through an invertible operation. Such a scheme could guarantee the
fidelity of dense point recovery from sampled points. Extensive experiments
demonstrate that the proposed PointLIE outperforms state-of-the-arts both
quantitatively and qualitatively. Our code is released through
this https URL

本研究提出了一种基于深度学习的点云自适应采样和恢复方法称为点云局部可逆嵌入（PointLIE），实现了点云采样和重建的统一框架，通过双向学习，提高了采样点的恢复质量，并在定量和定性方面均优于现有的技术。