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Line clouds, though under-investigated in the previous work, potentiallyencode more compact structural information of buildings than point cloudsextracted from multi-view images. In this work, we propose the first network toprocess line clouds for building wireframe abstraction. The network takes aline cloud as input , i.e., a nonstructural and unordered set of 3D linesegments extracted from multi-view images, and outputs a 3D wireframe of theunderlying building, which consists of a sparse set of 3D junctions connectedby line segments. We observe that a line patch, i.e., a group of neighboringline segments, encodes sufficient contour information to predict the existenceand even the 3D position of a potential junction, as well as the likelihood ofconnectivity between two query junctions. We therefore introduce a two-layerLine-Patch Transformer to extract junctions and connectivities from sampledline patches to form a 3D building wireframe model. We also introduce asynthetic dataset of multi-view images with ground-truth 3D wireframe. Weextensively justify that our reconstructed 3D wireframe models significantlyimprove upon multiple baseline building reconstruction methods. The code anddata can be found at https://github.com/Luo1Cheng/LC2WF.

Learning to Construct 3D Building Wireframes from 3D Line Clouds