We present a sketch-based CAD modeling system, where users create objects incrementally by sketching the desired shape edits, which our system automatically translates to CAD operations. Our approach is motivated by the close similarities between the steps industrial designers follow to draw 3D shapes, and the operations CAD modeling systems offer to create similar shapes. To overcome the strong ambiguity with parsing 2D sketches, we observe that in a sketching sequence, each step makes sense and can be interpreted in the context of what has been drawn before. In our system, this context corresponds to a partial CAD model, inferred in the previous steps, which we feed along with the input sketch to a deep neural network in charge of interpreting how the model should be modified by that sketch. Our deep network architecture then recognizes the intended CAD operation and segments the sketch accordingly, such that a subsequent optimization estimates the parameters of the operation that best fit the segmented sketch strokes. Since there exists no datasets of paired sketching and CAD modeling sequences, we train our system by generating synthetic sequences of CAD operations that we render as line drawings. We present a proof of concept realization of our algorithm supporting four frequently used CAD operations. Using our system, participants are able to quickly model a large and diverse set of objects, demonstrating Sketch2CAD to be an alternate way of interacting with current CAD modeling systems.

中文翻译：

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Sketch2CAD

我们提出了一个基于草图的 CAD 建模系统，用户通过绘制所需的形状编辑来逐步创建对象，我们的系统会自动将其转换为 CAD 操作。我们的方法是由工业设计师绘制 3D 形状所遵循的步骤与 CAD 建模系统提供的用于创建相似形状的操作之间的密切相似性所推动的。为了克服解析 2D 草图的强烈歧义，我们观察到在草图序列中，每个步骤都有意义并且可以在语境以前画过的东西。在我们的系统中，该上下文对应于在前面的步骤中推断出的部分 CAD 模型，我们将其与输入草图一起提供给深度神经网络，该网络负责解释该草图应如何修改模型。然后，我们的深度网络架构识别预期的 CAD 操作并相应地分割草图，以便后续优化估计最适合分割的草图笔划的操作参数。由于不存在成对的草图和 CAD 建模序列的数据集，我们通过生成我们渲染为线条图的 CAD 操作的合成序列来训练我们的系统。我们展示了我们的算法的概念实现证明，它支持四种常用的 CAD 操作。使用我们的系统，