Strokes2Surface: Recovering Curve Networks From 4D Architectural Design Sketches

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Advanced Computational Design
• Date: May 2024
• Article Number: e15054
• DOI: 10.1111/cgf.15054
• ISSN: 1467-8659
• Journal: Computer Graphics Forum
• Number: 2
• Pages: 16
• Volume: 43
• Publisher: WILEY
• Pages: 1 – 16
• Keywords: CCS Concepts, Computer graphics, Computing methodologies → Artificial intelligence, Machine learning

Abstract

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.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1111/cgf.15054

BibTeX

@article{rasoulzadeh-2024-strokes2surface,
  title =      "Strokes2Surface: Recovering Curve Networks From 4D
               Architectural Design Sketches",
  author =     "Shervin Rasoulzadeh and Michael Wimmer and Philipp Stau{\ss}
               and Iva Kovacic",
  year =       "2024",
  abstract =   "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.",
  month =      may,
  articleno =  "e15054",
  doi =        "10.1111/cgf.15054",
  issn =       "1467-8659",
  journal =    "Computer Graphics Forum",
  number =     "2",
  pages =      "16",
  volume =     "43",
  publisher =  "WILEY",
  pages =      "1--16",
  keywords =   "CCS Concepts, Computer graphics, Computing methodologies →
               Artificial intelligence, Machine learning",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/rasoulzadeh-2024-strokes2surface/",
}