Dream Lens: Exploration and Visualization of Large-Scale Generative Design Datasets (3:06 min.)
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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.
Visual data representations leverage the power of human perception to process complex information, and through interaction, garner new insights. Our research focuses on visualizing data from a wide variety of domains and fundamentally tackles the question, what makes a visualization effective? We explore novel visual encodings and interaction techniques, multiscale approaches, and even simulation to bridge human and automated analysis of multivariate, time-series, and graph data, ultimately aiding in hypothesis generation, testing, and sense making.