types considered in this work

Note that in the current implementation we do not distinguish sub-primitive references that point to different parts of a primitive, but rely on the predicted geometric closeness of primitive parts to tell them in the post-process, as we find the geometric predictions are generally quite accurate for this purpose. On the other hand, we note that the extension of references into primitive parts can be trivially achieved by turning primitives into functions and augmenting them with arguments (similar to how we model constraints), such that each argument corresponds to a primitive part; the constraint references can then pinpoint to primitive parts through argument passing (Sec. A.2 Implementation details Sketch encoding format In Sec. 4 we described how sketches are encoded to allow network learning; here we present more implementation details. We encode the input sketch S as a series of primitive tokens followed by a series of constraint tokens, with these tokens supplemented by learned positional encoding according to their indices in this sequence (Sec. We additionally insert learnable START, END and NEW tokens at the front of the sequence, the end of the sequence, as well as between every encoded primitive/constraint respectively, to produce the complete sequence.