We study the problem of extracting correspondences between a pair of point
clouds for registration. For correspondence retrieval, existing works benefit
from matching sparse keypoints detected from dense points but usually struggle
to guarantee their repeatability. To address this issue, we present CoFiNet -
Coarse-to-Fine Network which extracts hierarchical correspondences from coarse
to fine without keypoint detection. On a coarse scale and guided by a weighting
scheme, our model firstly learns to match down-sampled nodes whose vicinity
points share more overlap, which significantly shrinks the search space of a
consecutive stage. On a finer scale, node proposals are consecutively expanded
to patches that consist of groups of points together with associated
descriptors. Point correspondences are then refined from the overlap areas of
corresponding patches, by a density-adaptive matching module capable to deal
with varying point density. Extensive evaluation of CoFiNet on both indoor and
outdoor standard benchmarks shows our superiority over existing methods.
Especially on 3DLoMatch where point clouds share less overlap, CoFiNet
significantly outperforms state-of-the-art approaches by at least 5% on
Registration Recall, with at most two-third of their parameters.

本文介绍了一种名为 CoFiNet 的粗糙到精细网络技术，该技术无需进行关键点检测，而是通过权重方案从粗糙到精细地提取点云之间的对应关系并进行匹配。在标准测试中，CoFiNet 在室内和室外场景上都比现有方法表现优越，特别是在 3DLoMatch 上，其 Registration Recall 性能提高至少 5％，而参数至多只有现有方法的三分之二。

CoFiNet：可靠的粗到细的对应关系用于鲁棒的点云配准

CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud  Registration

Airborne discrete return light detection and ranging (LiDAR) point clouds
covering forested areas can be processed to segment individual trees and
retrieve their morphological attributes. Segmenting individual trees in natural
deciduous forests however remained a challenge because of the complex and
multi-layered canopy. In this chapter, we present (i) a robust segmentation
method that avoids a priori assumptions about the canopy structure, (ii) a
vertical canopy stratification procedure that improves segmentation of
understory trees, (iii) an occlusion model for estimating the point density of
each canopy stratum, and (iv) a distributed computing approach for efficient
processing at the forest level. When applied to the University of Kentucky
Robinson Forest, the segmentation method detected about 90% of overstory and
47% of understory trees with over-segmentation rates of 14% and 2%. Stratifying
the canopy improved the detection rate of understory trees to 68% at the cost
of increasing their over-segmentations to 16%. According to our occlusion
model, a point density of ~170 pt/m-sqr is needed to segment understory trees
as accurately as overstory trees. Lastly, using the distributed approach, we
segmented about two million trees in the 7,440-ha forest in 2.5 hours using 192
processors, which is 167 times faster than using a single processor. Keywords:
individual tree segmentation, multi-layered stand, vertical canopy
stratification, segmentation evaluation, point density, canopy occlusion
effect, big data, distributed computing.

介绍了一种用于处理森林点云的方法，通过用垂直平面对森林按层次分层，估算每个层次的点密度、训练出一个避免先验假设和复杂模型的树木分割模型，并采用分布式计算对大型森林进行高效处理。

利用离散回波机载激光雷达遥感森林

Remote sensing of forests using discrete return airborne LiDAR

Airborne laser scanning (LiDAR) point clouds over large forested areas can be
processed to segment individual trees and subsequently extract tree-level
information. Existing segmentation procedures typically detect more than 90% of
overstory trees, yet they barely detect 60% of understory trees because of the
occlusion effect of higher canopy layers. Although understory trees provide
limited financial value, they are an essential component of ecosystem
functioning by offering habitat for numerous wildlife species and influencing
stand development. Here we model the occlusion effect in terms of point
density. We estimate the fractions of points representing different canopy
layers (one overstory and multiple understory) and also pinpoint the required
density for reasonable tree segmentation (where accuracy plateaus). We show
that at a density of ~170 pt/m-sqr understory trees can likely be segmented as
accurately as overstory trees. Given the advancements of LiDAR sensor
technology, point clouds will affordably reach this required density. Using
modern computational approaches for big data, the denser point clouds can
efficiently be processed to ultimately allow accurate remote quantification of
forest resources. The methodology can also be adopted for other similar remote
sensing or advanced imaging applications such as geological subsurface
modelling or biomedical tissue analysis.

本文研究利用激光雷达技术 (LiDAR) 建立森林树木分割模型，通过点密度实现森林资源的远程精确量化。同时，该方法也可以应用于其他远程感应技术以及高级成像技术，如地质亚表面建模或生物医学组织分析。