Labels on Levels: Labeling of Multi-Scale Multi-Instance and Crowded 3D Biological Environments

David Kouřil, Ladislav Čmolík, Barbora Kozlikova, Hsiang-Yun Wu, Graham Johnson, David Goodsell, Arthur Olson, Meister Eduard Gröller, Ivan Viola
Labels on Levels: Labeling of Multi-Scale Multi-Instance and Crowded 3D Biological Environments
IEEE Transactions on Visualization and Computer Graphics, January 2019. [paper]

Information

Abstract

Labeling is intrinsically important for exploring and understanding complex environments and models in a variety of domains. We present a method for interactive labeling of crowded 3D scenes containing very many instances of objects spanning multiple scales in size. In contrast to previous labeling methods, we target cases where many instances of dozens of types are present and where the hierarchical structure of the objects in the scene presents an opportunity to choose the most suitable level for each placed label. Our solution builds on and goes beyond labeling techniques in medical 3D visualization, cartography, and biological illustrations from books and prints. In contrast to these techniques, the main characteristics of our new technique are: 1) a novel way of labeling objects as part of a bigger structure when appropriate, 2) visual clutter reduction by labeling only representative instances for each type of an object, and a strategy of selecting those. The appropriate level of label is chosen by analyzing the scene's depth buffer and the scene objects' hierarchy tree. We address the topic of communicating the parent-children relationship between labels by employing visual hierarchy concepts adapted from graphic design. Selecting representative instances considers several criteria tailored to the character of the data and is combined with a greedy optimization approach. We demonstrate the usage of our method with models from mesoscale biology where these two characteristics-multi-scale and multi-instance-are abundant, along with the fact that these scenes are extraordinarily dense.

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BibTeX

@article{kouril-2018-LoL,
  title =      "Labels on Levels: Labeling of Multi-Scale Multi-Instance and
               Crowded 3D Biological Environments",
  author =     "David Kou\v{r}il and Ladislav \v{C}mol\'{i}k and Barbora
               Kozlikova and Hsiang-Yun Wu and Graham Johnson and David
               Goodsell and Arthur Olson and Meister Eduard Gr\"{o}ller and
               Ivan Viola",
  year =       "2019",
  abstract =   "Labeling is intrinsically important for exploring and
               understanding complex environments and models in a variety
               of domains. We present a method for interactive labeling of
               crowded 3D scenes containing very many instances of objects
               spanning multiple scales in size. In contrast to previous
               labeling methods, we target cases where many instances of
               dozens of types are present and where the hierarchical
               structure of the objects in the scene presents an
               opportunity to choose the most suitable level for each
               placed label. Our solution builds on and goes beyond
               labeling techniques in medical 3D visualization,
               cartography, and biological illustrations from books and
               prints. In contrast to these techniques, the main
               characteristics of our new technique are: 1) a novel way of
               labeling objects as part of a bigger structure when
               appropriate, 2) visual clutter reduction by labeling only
               representative instances for each type of an object, and a
               strategy of selecting those. The appropriate level of label
               is chosen by analyzing the scene's depth buffer and the
               scene objects' hierarchy tree. We address the topic of
               communicating the parent-children relationship between
               labels by employing visual hierarchy concepts adapted from
               graphic design. Selecting representative instances considers
               several criteria tailored to the character of the data and
               is combined with a greedy optimization approach. We
               demonstrate the usage of our method with models from
               mesoscale biology where these two
               characteristics-multi-scale and multi-instance-are abundant,
               along with the fact that these scenes are extraordinarily
               dense.",
  month =      jan,
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  note =       "SciVis Best Paper Honorable Mention",
  keywords =   "labeling, multi-scale data, multi-instance data",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2019/kouril-2018-LoL/",
}