Dream Lens: Exploration and Visualization of Large-Scale Generative Design Datasets

Justin Matejka, Michael Glueck, Erin Bradner, Ali Hashemi, Tovi Grossman, George Fitzmaurice

January 2018 · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI)

Abstract

This paper presents Dream Lens, an interactive visual analysis tool for exploring and visualizing large-scale generative design datasets. Unlike traditional computer aided design, where users create a single model, with generative design, users specify high-level goals and constraints, and the system automatically generates hundreds or thousands of candidates all meeting the design criteria. Once a large collection of design variations is created, the designer is left with the task of finding the design, or set of designs, which best meets their requirements. This is a complicated task which could require analyzing the structural characteristics and visual aesthetics of the designs. Two studies are conducted which demonstrate the usability and usefulness of the Dream Lens system, and a generatively designed dataset of 16,800 designs for a sample design problem is described and publicly released to encourage advancement in this area.

DOI PDF

Figures

Figure 1. Conceptual illustration of a collection of design variations for a single task: lifting a computer monitor 80mm off a desk.

Figure 2. A selection of objects using a divergent generative design approach. From left to right: airplane partition, truss-based chair, office layout, and the Elbo chair.

Figure 3. Sample design task, raising a computer monitor off the surface of a desk.

Figure 4. Problem definition describing the locations of the feet, platforms, and desk surface geometry, and the position and direction of the static forces.

sure 5. A single design, produced with middle and ou ids of 500N each.

Figure 7. Three example Single-Attribute Controllers. In the bottom one, a selection has been made, as indicated by the ‘x’ button, which will clear the selection.

Figure 8. The full collection of single-attribute controls, separated in two columns.

Figure 9. Attribute Example view for Center of Mass X.

— wa Figure 10. Representative designs for, Middle Load and Overhang Percentage.

Figure 11. Multi-Attribute Grid (MAG) showing the relationship between Weight and Total Load.

Figure 12. The design viewer with the initial (top left), and filtered views with 1242, 186, 77, and 6 design options.

Figure 13. A design tooltip displayed when the cursor is over a model thumbnail.

Figure 14. The standard 3D grid view (left), animating to the stacked view (right).

Figure 15. Sample stacked views for 4 (left) and 100 (right), similar (top) and dissimilar (bottom) designs.

Figure 16. The effects of the chisel, select, and edge tools in the stacked model mode.

Figure 17. Graphical representation of the edge tool.

Figure 18. Steps involved in creating a ranking (A-C), and optionally, adjusting the importance weightings of the various attributes (D).

Figure 19. Top Nine view for the specified ranking.

igure 20. Steps to find those designs which best combine < yw weight with a low overhang percentage.

Figure 21. Possible workflow for reconciling preferences of multiple stakeholders.

Figure 22. Possible steps for finding “non-standard” results within the dataset.

Figure 23. The task descriptions and completion times from the lab study. Black bar indicates median completion times.

BibTeX

@inproceedings{10.1145/3173574.3173943,
 abstract = {This paper presents Dream Lens, an interactive visual analysis tool for exploring and visualizing large-scale generative design datasets. Unlike traditional computer aided design, where users create a single model, with generative design, users specify high-level goals and constraints, and the system automatically generates hundreds or thousands of candidates all meeting the design criteria. Once a large collection of design variations is created, the designer is left with the task of finding the design, or set of designs, which best meets their requirements. This is a complicated task which could require analyzing the structural characteristics and visual aesthetics of the designs. Two studies are conducted which demonstrate the usability and usefulness of the Dream Lens system, and a generatively designed dataset of 16,800 designs for a sample design problem is described and publicly released to encourage advancement in this area.},
 address = {New York, NY, USA},
 author = {Matejka, Justin and Glueck, Michael and Bradner, Erin and Hashemi, Ali and Grossman, Tovi and Fitzmaurice, George},
 booktitle = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems},
 doi = {10.1145/3173574.3173943},
 isbn = {9781450356206},
 keywords = {interaction, generative design, visualization},
 location = {Montreal QC, Canada},
 numpages = {12},
 pages = {1–12},
 publisher = {Association for Computing Machinery},
 series = {CHI '18},
 title = {Dream Lens: Exploration and Visualization of Large-Scale Generative Design Datasets},
 url = {https://doi.org/10.1145/3173574.3173943},
 year = {2018}
}