Aggregate Point Cloud Geometric Features for Processing

As 3D acquisition technology develops and 3D sensors become increasingly affordable,large quantities of 3D point cloud data are emerging.How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved.The point cloud geometric information is hidden in disordered,unstructured points,making point cloud analysis a very challenging problem.To address this problem,we propose a novel network framework,called Tree Graph Network(TGNet),which can sample,group,and aggregate local geometric features.Specifically,we construct a Tree Graph by explicit rules,which consists of curves extending in all directions in point cloud feature space,and then aggregate the features of the graph through a cross-attention mechanism.In this way,we incorporate more point cloud geometric structure information into the representation of local geometric features,which makes our network perform better.Our model performs well on several basic point clouds processing tasks such as classification,segmentation,and normal estimation,demonstrating the effectiveness and superiority of our network.Furthermore,we provide ablation experiments and visualizations to better understand our network.

基材冷却状态对激光沉积AlSi10Mg合金成形质量的影响

为研究基材冷却状态对激光沉积成形AlSi10Mg合金质量和性能的影响,在不同冷却条件下沉积成形AlSi10Mg合金试样。利用温度测量系统、金相显微镜、扫描电镜、万能材料试验机对成形过程中温度演变和成形后试样宏观形貌、组织状态、拉伸性能等进行分析。结果表明:合理的基体冷却温度会使沉积效率提升,缺陷率降低;改变冷却条件可以使沉积层组织发生显著变化,内部的枝晶间距明显减小。优化冷却条件后沉积试样的屈服强度和抗拉强度较无冷却条件下的分别提升了约4%和7%,其断裂方式均为韧性断裂。