  We present MV2Cyl, a novel method for reconstructing 3D from 2D multi-view
images, not merely as a field or raw geometry but as a sketch-extrude CAD
model. Extracting extrusion cylinders from raw 3D geometry has been extensively
researched in computer vision, while the processing of 3D data through neural
networks has remained a bottleneck. Since 3D scans are generally accompanied by
multi-view images, leveraging 2D convolutional neural networks allows these
images to be exploited as a rich source for extracting extrusion cylinder
information. However, we observe that extracting only the surface information
of the extrudes and utilizing it results in suboptimal outcomes due to the
challenges in the occlusion and surface segmentation. By synergizing with the
extracted base curve information, we achieve the optimal reconstruction result
with the best accuracy in 2D sketch and extrude parameter estimation. Our
experiments, comparing our method with previous work that takes a raw 3D point
cloud as input, demonstrate the effectiveness of our approach by taking
advantage of multi-view images.
