从圆形体素模型重建可编辑棱柱体CAD
Reconstructing editable prismatic CAD from rounded voxel models
摘要：
从其他表示法对CAD形状进行逆向工程是许多下游应用程序的重要几何处理步骤。在这项工作中，我们引入了一种新的神经网络架构来解决这个具有挑战性的任务，并近似平滑的有符号距离函数与可编辑的，受约束的，棱柱形的CAD模型。在训练过程中，我们的方法通过将形状分解为一系列2D轮廓图像和1D包络函数来重建体素空间中的输入几何形状。然后，这些可以以可微分的方式重新组合，从而允许定义几何损失函数。在推理过程中，我们首先搜索二维约束草图数据库，找到近似轮廓图像的曲线，然后拉伸它们，并使用布尔运算来构建最终的CAD模型。我们的方法比其他方法更接近目标形状，并输出高度可编辑的约束参数化草图，与现有的CAD软件兼容。
英文摘要：
Reverse Engineering a CAD shape from other representations is an important geometric processing step for many downstream applications. In this work, we introduce a novel neural network architecture to solve this challenging task and approximate a smoothed signed distance function with an editable, constrained, prismatic CAD model. During training, our method reconstructs the input geometry in the voxel space by decomposing the shape into a series of 2D profile images and 1D envelope functions. These can then be recombined in a differentiable way allowing a geometric loss function to be defined. During inference, we obtain the CAD data by first searching a database of 2D constrained sketches to find curves which approximate the profile images, then extrude them and use Boolean operations to build the final CAD model. Our method approximates the target shape more closely than other methods and outputs highly editable constrained parametric sketches which are compatible with existing CAD software.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
