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OpenSketch: A Richly-Annotated Dataset of Product Design Sketches
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OpenSketch: A Richly-Annotated Dataset of Product Design Sketches

ACM Transactions on Graphics (SIGGRAPH Asia Conference Proceedings), Volume 38, Number 6 - November 2019
Download the publication : Gryaditskaya_OpenSketch_AuthorsVersion.pdf [66.5Mo]  
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: https://ns.inria.fr/d3/OpenSketch.

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See also

Data and source code

Webpages with the dataset and source code links.

Video

Acknowledgements and Funding

This work was supported by the ERC starting grant D3 (ERC-2016-STG 714221). We thank the reviewers for their multiple suggestions. We express our special gratefulness to Bastien Wailly for preparing the synthetic datasets to train Sketch2Normal, and to Julien Wintz, Nicolas Niclausse and Marc Vesin for setting up the server for our sketching interface, and advising on Node.js and MariaDB usage. We thank Valentin Deschaintre for technical discussions. Special thanks also to Pascal Barla and Yotam Gingold for feedback on preliminary versions of this work. We thank Johanna Delanoy, George Drettakis, George Alex Koulieris and Tibor Stanko for proofreading our paper. We thank also Jean Marc Fuselier, Maurille Lariviere and the students of The Sustainable Design School for participating in our pilot study. Finally, we thank all the designers and design students who contributed to our dataset.

BibTex references

@Article{GSHPDB19,
  author       = "Gryaditskaya, Yulia and Sypesteyn , Mark and Hoftijzer, Jan Willem and Pont, Sylvia and Durand, Fr\'edo and Bousseau, Adrien",
  title        = "OpenSketch: A Richly-Annotated Dataset of Product Design Sketches",
  journal      = "ACM Transactions on Graphics (SIGGRAPH Asia Conference Proceedings)",
  number       = "6",
  volume       = "38",
  month        = "November",
  year         = "2019",
  keywords     = "product design, sketching, line drawing, sketch-based modeling, non-photorealistic rendering, dataset",
  url          = "http://www-sop.inria.fr/reves/Basilic/2019/GSHPDB19"
}

Other publications in the database

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