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.

翻译：本文提出MV2Cyl，一种从2D多视角图像重建3D模型的新方法，其目标并非生成场表示或原始几何，而是构建草图拉伸式CAD模型。从原始3D几何中提取拉伸圆柱体在计算机视觉领域已有广泛研究，而通过神经网络处理3D数据仍是瓶颈。由于3D扫描通常伴随多视角图像，利用2D卷积神经网络可将这些图像作为提取拉伸圆柱体信息的丰富来源。然而我们观察到，仅提取拉伸体表面信息并加以利用会导致次优结果，这源于遮挡与表面分割的挑战。通过结合提取的基线信息，我们在2D草图与拉伸参数估计中实现了最优重建精度。实验表明，与以往以原始3D点云为输入的方法相比，本方法通过利用多视角图像取得了更优的重建效果。