LiDAR scanning for surveying applications acquire measurements over wide
areas and long distances, which produces large-scale 3D point clouds with
significant local density variations. While existing 3D semantic segmentation
models conduct downsampling and upsampling to build robustness against varying
point densities, they are less effective under the large local density
variations characteristic of point clouds from surveying applications. To
alleviate this weakness, we propose a novel architecture called HDVNet that
contains a nested set of encoder-decoder pathways, each handling a specific
point density range. Limiting the interconnections between the feature maps
enables HDVNet to gauge the reliability of each feature based on the density of
a point, e.g., downweighting high density features not existing in low density
objects. By effectively handling input density variations, HDVNet outperforms
state-of-the-art models in segmentation accuracy on real point clouds with
inconsistent density, using just over half the weights.

本文提出了一种名为 HDVNet 的新型架构，针对测量应用程序中的点云具有的大局部密度变化提出解决方案，通过一个嵌套的编码器 - 解码器通路集合来处理特定的点密度范围，有效地处理了输入密度变化，提高了分割精度。

高密度变化的 3D 点云语义分割

Semantic Segmentation on 3D Point Clouds with High Density Variations

This paper presents a neural network for robust normal estimation on point
clouds, named AdaFit, that can deal with point clouds with noise and density
variations. Existing works use a network to learn point-wise weights for
weighted least squares surface fitting to estimate the normals, which has
difficulty in finding accurate normals in complex regions or containing noisy
points. By analyzing the step of weighted least squares surface fitting, we
find that it is hard to determine the polynomial order of the fitting surface
and the fitting surface is sensitive to outliers. To address these problems, we
propose a simple yet effective solution that adds an additional offset
prediction to improve the quality of normal estimation. Furthermore, in order
to take advantage of points from different neighborhood sizes, a novel Cascaded
Scale Aggregation layer is proposed to help the network predict more accurate
point-wise offsets and weights. Extensive experiments demonstrate that AdaFit
achieves state-of-the-art performance on both the synthetic PCPNet dataset and
the real-word SceneNN dataset.

该论文提出了一种神经网络 AdaFit，用于在含有噪声和密度变化的点云上进行强健的法线估计，并通过实验证明其能够在合成数据集和真实场景数据集上实现最好的性能。