Honorable Mention

Dr. KID: Direct Remeshing and K-set Isometric Decomposition for Scalable Physicalization of Organic Shapes

Dawar Khan, Ciril Bohak, Ivan Viola

Room: 103

2023-10-25T22:00:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
The physicalization of potato-shaped biological structures with k types of triangles. Back row: SARS-CoV-2 virion membrane (left) with k = 2, SARS-CoV-2 virion membrane with smooth triangle patches (right), using k = 6, and front row: cell nuclei membrane (left), using k = 5, SARS-CoV-2 virion membrane (center), using k = 2, mitochondria outer membrane (right), using k = 6.
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Physicalization, Physical visualization, 3D printing, Isometric decomposition, Direct remeshing, Biological structures, Intracellular compartments.


Dr. KID is an algorithm that uses isometric decomposition for the physicalization of potato-shaped organic models in a puzzle fashion. The algorithm begins with creating a simple, regular triangular surface mesh of organic shapes, followed by iterative K-means clustering and remeshing. For clustering, we need similarity between triangles (segments) which is defined as a distance function. The distance function maps each triangle's shape to a single point in the virtual 3D space. Thus, the distance between the triangles indicates their degree of dissimilarity. K-means clustering uses this distance and sorts segments into k classes. After this, remeshing is applied to minimize the distance between triangles within the same cluster by making their shapes identical. Clustering and remeshing are repeated until the distance between triangles in the same cluster reaches an acceptable threshold. We adopt a curvature-aware strategy to determine the surface thickness and finalize puzzle pieces for 3D printing. Identical hinges and holes are created for assembling the puzzle components. For smoother outcomes, we use triangle subdivision along with curvature-aware clustering, generating curved triangular patches for 3D printing. Our algorithm was evaluated using various models, and the 3D-printed results were analyzed. Findings indicate that our algorithm performs reliably on target organic shapes with minimal loss of input geometry.