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We present a sketch-based CAD modeling system, where users create objectsincrementally by sketching the desired shape edits, which our systemautomatically translates to CAD operations. Our approach is motivated by theclose similarities between the steps industrial designers follow to draw 3Dshapes, and the operations CAD modeling systems offer to create similar shapes.To overcome the strong ambiguity with parsing 2D sketches, we observe that in asketching sequence, each step makes sense and can be interpreted in the\emph{context} of what has been drawn before. In our system, this contextcorresponds to a partial CAD model, inferred in the previous steps, which wefeed along with the input sketch to a deep neural network in charge ofinterpreting how the model should be modified by that sketch. Our deep networkarchitecture then recognizes the intended CAD operation and segments the sketchaccordingly, such that a subsequent optimization estimates the parameters ofthe operation that best fit the segmented sketch strokes. Since there exists nodatasets of paired sketching and CAD modeling sequences, we train our system bygenerating synthetic sequences of CAD operations that we render as linedrawings. We present a proof of concept realization of our algorithm supportingfour frequently used CAD operations. Using our system, participants are able toquickly model a large and diverse set of objects, demonstrating Sketch2CAD tobe an alternate way of interacting with current CAD modeling systems.

Sketch2CAD: Sequential CAD Modeling by Sketching in Context