Precise segmentation of architectural structures provides detailed information about various building components, enhancing our understanding and interaction with our built environment. Nevertheless, existing outdoor 3D point cloud datasets have limited and detailed annotations on architectural exteriors due to privacy concerns and the expensive costs of data acquisition and annotation. To overcome this shortfall, this paper introduces a semantically-enriched, photo-realistic 3D architectural models dataset and benchmark for semantic segmentation. It features 4 different building purposes of real-world buildings as well as an open architectural landscape in Hong Kong. Each point cloud is annotated into one of 14 semantic classes.
Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution
Tokyo Institute of Technology（东京工业大学）；Artificial Intelligence Research Center, AIST（产业技术综合研究所人工智能研究中心）；Dalian University of Technology（大连理工大学）；PASCO Corporation（PASCO公司）
Less is More: Efficient Point Cloud Reconstruction via Multi-Head Decoders
School of Computing and Artificial Intelligence, Southwest Jiaotong University（计算机与人工智能学院，西南交通大学）；Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education, Chengdu, China（可持续城市智能交通工程研究中心，教育部，成都，中国）
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds
Stanford University（斯坦福大学）；University of California, Davis（加州大学戴维斯分校）；TuSimple（图森未来）
我们提出了一种新的深度神经网络架构，用于直接对大规模3D点云进行端到端场景流估计。受双边卷积层（BCL）的启发，我们提出了新的DownBCL，UpBCL和CorrBCL操作，从非结构化点云恢复结构信息，并融合来自两个连续点云的信息。操作离散和稀疏的permutohedral格点，我们的架构设计是节俭的计算成本。我们的模型可以有效地处理一对点云帧，每帧最多86K个点。我们的方法在FlyingThings3D和KITTI Scene Flow 2015数据集上实现了最先进的性能。此外，在合成数据上训练，我们的方法对真实世界的数据和不同的点密度表现出很强的泛化能力，而无需微调。
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）
SuperiorGAT: Graph Attention Networks for Sparse LiDAR Point Cloud Reconstruction in Autonomous Systems
Electrical and Computer Engineering, Portland State University（电气与计算机工程系，波特兰州立大学）；Computational Data Science and Engineering Department, North Carolina A&T State University（计算数据科学与工程系，北卡罗来纳A&T州立大学）；Electrical and Computer Engineering Department, SUNY Oswego（电气与计算机工程系，SUNY奥威戈）
