Pipes are the basic building block in many industrial sites like electricity and chemical plants. Although pipes are merely cylindrical primitives which can be defined by axis and radius, they often consist of additional components like flanges, valves, elbows, tees, etc. 3D pipes are typically dense, consisting of a wide range of topologies and geometries, with large self-occlusions. Thus, reconstruction of a coherent 3D pipe models from large-scale point clouds is a challenging problem. In this work we take a prior-based reconstruction approach which reduces the complexity of the general pipe reconstruction problem into a combination of part detection and model fitting problems. We utilize convolutional network to learn point cloud features and classify points into various classes, then apply robust clustering and graph-based aggregation techniques to compute a coherent pipe model. Our method shows promising results on pipe models with varying complexity and density both in synthetic and real cases.

管道是许多工业场所（如电力和化工厂）的基本构件。尽管管道只是可以通过轴和半径定义的圆柱图元，但它们通常由法兰，阀门，弯头，三通等附加组件组成。3D管道通常是密集的，由多种拓扑和几何形状组成，且尺寸较大自我遮挡。因此，从大规模点云重建相干3D管道模型是一个具有挑战性的问题。在这项工作中，我们采用基于先验的重构方法，该方法将一般管道重构问题的复杂性降低为零件检测和模型拟合问题的组合。我们利用卷积网络来学习点云特征并将点分类为各种类别，然后应用鲁棒的聚类和基于图的聚合技术来计算一致的管道模型。我们的方法在合成和实际情况下在复杂性和密度不同的管道模型上显示出令人鼓舞的结果。