Remote sensing through unmanned aerial systems (UAS) has been increasing in
forestry in recent years, along with using machine learning for data
processing. Deep learning architectures, extensively applied in natural
language and image processing, have recently been extended to the point cloud
domain. However, the availability of point cloud datasets for training and
testing remains limited. Creating forested environment point cloud datasets is
expensive, requires high-precision sensors, and is time-consuming as manual
point classification is required. Moreover, forest areas could be inaccessible
or dangerous for humans, further complicating data collection. Then, a question
arises whether it is possible to use synthetic data to train deep learning
networks without the need to rely on large volumes of real forest data. To
answer this question, we developed a realistic simulator that procedurally
generates synthetic forest scenes. Thanks to this, we have conducted a
comparative study of different state-of-the-art point-based deep learning
networks for forest segmentation. Using created datasets, we determined the
feasibility of using synthetic data to train deep learning networks to classify
point clouds from real forest datasets. Both the simulator and the datasets are
released as part of this work.

通过使用合成数据集，我们开发了一个逼真的模拟器，生成了合成的森林场景，用于开展不同基于点云的深度学习网络在森林分割方面的比较研究，并确定了使用合成数据来训练深度学习网络对真实森林数据集中的点云进行分类的可行性。

使用合成数据训练基于点的深度学习网络进行森林分割

Training point-based deep learning networks for forest segmentation with  synthetic data

This paper presents a novel point cloud compression method COT-PCC by
formulating the task as a constrained optimal transport (COT) problem. COT-PCC
takes the bitrate of compressed features as an extra constraint of optimal
transport (OT) which learns the distribution transformation between original
and reconstructed points. Specifically, the formulated COT is implemented with
a generative adversarial network (GAN) and a bitrate loss for training. The
discriminator measures the Wasserstein distance between input and reconstructed
points, and a generator calculates the optimal mapping between distributions of
input and reconstructed point cloud. Moreover, we introduce a learnable
sampling module for downsampling in the compression procedure. Extensive
results on both sparse and dense point cloud datasets demonstrate that COT-PCC
outperforms state-of-the-art methods in terms of both CD and PSNR metrics.
Source codes are available at https://github.com/cognaclee/PCC-COT.

该论文介绍了一种新颖的点云压缩方法 COT-PCC，通过将任务作为约束最优传输（COT）问题进行建模，COT-PCC 利用压缩特征的比特率作为最优传输（OT）的额外约束，学习原始和重构点之间的分布转换。具体而言，通过生成对抗网络（GAN）和比特率损失对 COT 进行实现，鉴别器测量输入和重构点之间的 Wasserstein 距离，并且生成器计算输入和重构点云分布之间的最优映射。此外，论文还引入了可学习的采样模块用于压缩过程中的下采样。在稀疏和密集点云数据集上进行的大量实验结果表明，COT-PCC 在 CD 和 PSNR 指标方面优于现有最先进方法。源代码可在 https://github.com/cognaclee/PCC-COT 获得。

基于约束最优传输的点云压缩

Point Cloud Compression via Constrained Optimal Transport

Point cloud datasets often suffer from inadequate sample sizes in comparison
to image datasets, making data augmentation challenging. While traditional
methods, like rigid transformations and scaling, have limited potential in
increasing dataset diversity due to their constraints on altering individual
sample shapes, we introduce the Biharmonic Augmentation (BA) method. BA is a
novel and efficient data augmentation technique that diversifies point cloud
data by imposing smooth non-rigid deformations on existing 3D structures. This
approach calculates biharmonic coordinates for the deformation function and
learns diverse deformation prototypes. Utilizing a CoefNet, our method predicts
coefficients to amalgamate these prototypes, ensuring comprehensive
deformation. Moreover, we present AdvTune, an advanced online augmentation
system that integrates adversarial training. This system synergistically
refines the CoefNet and the classification network, facilitating the automated
creation of adaptive shape deformations contingent on the learner status.
Comprehensive experimental analysis validates the superiority of Biharmonic
Augmentation, showcasing notable performance improvements over prevailing point
cloud augmentation techniques across varied network designs.

使用 Biharmonic augmentation 方法对点云数据进行数据增强，通过平滑的非刚性变形来改变 3D 结构，使用 Advtune 系统和对抗训练来自动创建适应性形态变形，实验证明 Biharmonic Augmentation 在多种网络设计上相比现有的点云增强技术有显著的性能提升。

基于学习的双调和增强点云分类

Learning-Based Biharmonic Augmentation for Point Cloud Classification

Deformable linear objects are vastly represented in our everyday lives. It is
often challenging even for humans to visually understand them, as the same
object can be entangled so that it appears completely different. Examples of
deformable linear objects include blood vessels and wiring harnesses, vital to
the functioning of their corresponding systems, such as the human body and a
vehicle. However, no point cloud datasets exist for studying 3D deformable
linear objects. Therefore, we are introducing two point cloud datasets,
PointWire and PointVessel. We evaluated state-of-the-art methods on the
proposed large-scale 3D deformable linear object benchmarks. Finally, we
analyzed the generalization capabilities of these methods by conducting
transferability experiments on the PointWire and PointVessel datasets.

介绍了两个点云数据集 PointWire 和 PointVessel，通过在这些数据集上进行评估和转移性实验，研究了 3D 可变形线状物体的最新方法及其泛化能力。