Although unsupervised feature learning has demonstrated its advantages to
reducing the workload of data labeling and network design in many fields,
existing unsupervised 3D learning methods still cannot offer a generic network
for various shape analysis tasks with competitive performance to supervised
methods. In this paper, we propose an unsupervised method for learning a
generic and efficient shape encoding network for different shape analysis
tasks. The key idea of our method is to jointly encode and learn shape and
point features from unlabeled 3D point clouds. For this purpose, we adapt
HR-Net to octree-based convolutional neural networks for jointly encoding shape
and point features with fused multiresolution subnetworks and design a
simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for
jointly learning the shape and point features. Our network takes a 3D point
cloud as input and output both shape and point features. After training, the
network is concatenated with simple task-specific back-end layers and
fine-tuned for different shape analysis tasks. We evaluate the efficacy and
generality of our method and validate our network and loss design with a set of
shape analysis tasks, including shape classification, semantic shape
segmentation, as well as shape registration tasks. With simple back-ends, our
network demonstrates the best performance among all unsupervised methods and
achieves competitive performance to supervised methods, especially in tasks
with a small labeled dataset. For fine-grained shape segmentation, our method
even surpasses existing supervised methods by a large margin.

本文提出了一种用于不同形状分析任务的通用和高效形状编码网络的无监督方法，具体方法是联合编码和学习来自未标记的三维点云的形状和点特征，使用基于八叉树的卷积神经网络来进行编码，并设计了一个简单而高效的多分辨率实例判别损失，本方法不仅表现出了所有无监督方法中最佳的性能，还在小标记数据集的任务中表现出了与监督方法有竞争力的性能，甚至在细粒度形状分割任务中超过了现有的监督方法。

多分辨率实例判别式的无监督三维形状分析学习

Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance  Discrimination

We achieve 3D semantic scene labeling by exploring semantic relation between
each point and its contextual neighbors through edges. Besides an
encoder-decoder branch for predicting point labels, we construct an edge branch
to hierarchically integrate point features and generate edge features. To
incorporate point features in the edge branch, we establish a hierarchical
graph framework, where the graph is initialized from a coarse layer and
gradually enriched along the point decoding process. For each edge in the final
graph, we predict a label to indicate the semantic consistency of the two
connected points to enhance point prediction. At different layers, edge
features are also fed into the corresponding point module to integrate
contextual information for message passing enhancement in local regions. The
two branches interact with each other and cooperate in segmentation. Decent
experimental results on several 3D semantic labeling datasets demonstrate the
effectiveness of our work.

本文介绍了一种利用边缘分支和层次化图框架实现点和其语境邻居之间的语义关系来实现 3D 语义场景标注的方法，并证明其有效性。

基于点边交互网络的点云语义分割

Hierarchical Point-Edge Interaction Network for Point Cloud Semantic  Segmentation

In this paper, we develop a robust efficient visual SLAM system that utilizes
heterogeneous point and line features. By leveraging ORB-SLAM [1], the proposed
system consists of stereo matching, frame tracking, local mapping, loop
detection, and bundle adjustment of both point and line features. In
particular, as the main theoretical contributions of this paper, we, for the
first time, employ the orthonormal representation as the minimal
parameterization to model line features along with point features in visual
SLAM and analytically derive the Jacobians of the re-projection errors with
respect to the line parameters, which significantly improves the SLAM solution.
The proposed SLAM has been extensively tested in both synthetic and real-world
experiments whose results demonstrate that the proposed system outperforms the
state-of-the-art methods in various scenarios.

本文提出了一种鲁棒性高、效率高的视觉 SLAM 系统，利用异构的点和线特征，并首次将正交表示作为线特征的最小参数化模型，并解析地推导了关于线参数的投影误差的雅克比矩阵，实验证明该方法在各种情况下都优于现有技术。