OpenSketch: A Richly-Annotated Dataset of Product Design Sketches

Yulia Gryaditskaya Univesité Côte d'Azur, Inria
Mark Sypesteyn Delft University of Technology, Faculty of Industrial Design Engineering
Jan Willem Hoftijzer Delft University of Technology, Faculty of Industrial Design Engineering
Sylvia Pont Delft University of Technology, Perceptual Intelligence lab
Frédo Durand Inria, MIT CSAIL
Adrien Bousseau Univesité Côte d'Azur, Inria
ACM Transactions on Graphics (Proc. SIGGRAPH Asia).


We showed designers three orthographic views (a) of the object and asked them to draw it from two different perspective views (b). We also asked to replicate each of their sketches as a clean presentation drawing (c). We semi-automatically registered 3D models to each sketch (d), and we manually labeled different types of lines in all concept sketches and presentation drawings from the first viewpoint (e). The sketches in this figure were done by Professional 5.


Product designers extensively use sketches to create and communicate 3D shapes and thus form an ideal audience for sketch-based modeling, nonphotorealistic rendering and sketch filtering. However, sketching requires significant expertise and time, making design sketches a scarce resource for the research community. We introduce OpenSketch, a dataset of product design sketches aimed at offering a rich source of information for a variety of computer-aided design tasks. OpenSketch contains more than 400 sketches representing 12 man-made objects drawn by 7 to 15 product designers of varying expertise. We provided participants with front, side and top views of these objects, and instructed them to draw from two novel perspective viewpoints. This drawing task forces designers to construct the shape from their mental vision rather than directly copy what they see. They achieve this task by employing a variety of sketching techniques and methods not observed in prior datasets. Together with industrial design teachers, we distilled a taxonomy of line types and used it to label each stroke of the 214 sketches drawn from one of the two viewpoints. While some of these lines have long been known in computer graphics, others remain to be reproduced algorithmically or exploited for shape inference. In addition, we also asked participants to produce clean presentation drawings from each of their sketches, resulting in aligned pairs of drawings of different styles. Finally, we registered each sketch to its reference 3D model by annotating sparse correspondences. We provide an analysis of our annotated sketches, which reveals systematic drawing strategies over time and shapes, as well as a positive correlation between presence of construction lines and accuracy. Our sketches, in combination with provided annotations, form challenging benchmarks for existing algorithms as well as a great source of inspiration for future developments. We illustrate the versatility of our data by using it to test a 3D reconstruction deep network trained on synthetic drawings, as well as to train a filtering network to convert concept sketches into presentation drawings. We distribute our dataset under the Creative Commons CC0 license:




  author       = "Yulia Gryaditskaya and Mark Sypesteyn and Jan Willem Hoftijzer and Sylvia Pont and Fr\'{e}do Durand and Adrien Bousseau",
  title        = "OpenSketch: A Richly-Annotated Dataset of Product Design Sketches",
  journal      = "ACM Transactions on Graphics (Proc. SIGGRAPH Asia)",
  year         = "2019",
  volume       = "38",
  month        = "11",
  publisher    = {ACM}