  We present Strokes2Surface, an offline geometry reconstruction pipeline that
recovers well-connected curve networks from imprecise 4D sketches to bridge
concept design and digital modeling stages in architectural design. The input
to our pipeline consists of 3D strokes' polyline vertices and their timestamps
as the 4th dimension, along with additional metadata recorded throughout
sketching. Inspired by architectural sketching practices, our pipeline combines
a classifier and two clustering models to achieve its goal. First, with a set
of extracted hand-engineered features from the sketch, the classifier
recognizes the type of individual strokes between those depicting boundaries
(Shape strokes) and those depicting enclosed areas (Scribble strokes). Next,
the two clustering models parse strokes of each type into distinct groups, each
representing an individual edge or face of the intended architectural object.
Curve networks are then formed through topology recovery of consolidated Shape
clusters and surfaced using Scribble clusters guiding the cycle discovery. Our
evaluation is threefold: We confirm the usability of the Strokes2Surface
pipeline in architectural design use cases via a user study, we validate our
choice of features via statistical analysis and ablation studies on our
collected dataset, and we compare our outputs against a range of
reconstructions computed using alternative methods.
