Name
Abstract | ||
|---|---|---|
People of all ages enjoy solving geometric puzzles. However, finding suitable puzzles, e.g., puzzles with a moderate level of difficulty or puzzles with intellectually stimulating shapes can be difficult. In addition, designing innovative and appealing puzzles requires demanding effort and, typically, involves many trial and error processes. In this paper, we introduce a computational approach for designing geometric puzzles. Existing approaches employ bottom-up, constructive algorithms to generate puzzle pieces; therefore, intervening in the piece generation procedure is difficult. Differing from existing approaches that generate puzzles automatically or semi-automatically, we propose a top-down, partitioning-based approach, that enables us to control and edit piece shapes. With a subtle modification, the proposed algorithm can be easily extended to both 3D polycube and 2D polyomino puzzle design. To generate a variety of piece shapes, the proposed approach involves a capacity-constrained graph partitioning algorithm combined with polyomino tiling. We demonstrate the versatility of the proposed approach through various example designs, including fabricated puzzles, created using the proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s00371-020-01968-5 | The Visual Computer |
Keywords | DocType | Volume |
Computational design, Graph partitioning, Puzzles | Journal | 37 |
Issue | ISSN | Citations |
4 | 0178-2789 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naoki Kita | 1 | 15 | 9.62 |
| Kazunori Miyata | 2 | 161 | 41.73 |