MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Images
This work addresses a bottleneck in processing 3D data for CAD model reconstruction from images, offering a domain-specific improvement for computer vision applications.
The paper tackles the problem of reconstructing 3D extrusion cylinders from multi-view images by proposing MV2Cyl, which leverages 2D CNNs to extract both surface and base curve information, achieving optimal accuracy in sketch and parameter estimation compared to methods using raw 3D point clouds.
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. Our project page can be found at http://mv2cyl.github.io .