Accurate and consistent construction of point clouds from LiDAR scanning data
is fundamental for 3D modeling applications. Current solutions, such as
multiview point cloud registration and LiDAR bundle adjustment, predominantly
depend on the local plane assumption, which may be inadequate in complex
environments lacking of planar geometries or substantial initial pose errors.
To mitigate this problem, this paper presents a LiDAR bundle adjustment with
progressive spatial smoothing, which is suitable for complex environments and
exhibits improved convergence capabilities. The proposed method consists of a
spatial smoothing module and a pose adjustment module, which combines the
benefits of local consistency and global accuracy. With the spatial smoothing
module, we can obtain robust and rich surface constraints employing smoothing
kernels across various scales. Then the pose adjustment module corrects all
poses utilizing the novel surface constraints. Ultimately, the proposed method
simultaneously achieves fine poses and parametric surfaces that can be directly
employed for high-quality point cloud reconstruction. The effectiveness and
robustness of our proposed approach have been validated on both simulation and
real-world datasets. The experimental results demonstrate that the proposed
method outperforms the existing methods and achieves better accuracy in complex
environments with low planar structures.

该论文介绍了一种具有渐进空间平滑的 LiDAR 束调整方法，用于复杂环境下的点云构建，能够获得高质量的点云重建结果，并在复杂环境中展现出更好的精度。

PSS-BA：具有渐进空间平滑的 LiDAR 束调整

PSS-BA: LiDAR Bundle Adjustment with Progressive Spatial Smoothing

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。