TY - JOUR
T1 - Topological Simplification of Nested Shapes
AU - Zeng, D.
AU - Chambers, E.
AU - Letscher, D.
AU - Ju, T.
N1 - Funding Information:
We appreciate the generous support from NSF (through grants DBI‐1759836, EF‐1921728, AF‐1907612 and AF‐2106672) and Washington University in St. Louis (through the Imaging Science Fellowship program). We would also like to thank Chris Topp at Donald Danforth Plant Science Center for providing the plant roots data set, and the anonymous reviewers for their suggestions.
Publisher Copyright:
© 2022 The Author(s) Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
PY - 2022/8
Y1 - 2022/8
N2 - We present a method for removing unwanted topological features (e.g., islands, handles, cavities) from a sequence of shapes where each shape is nested in the next. Such sequences can be found in nature, such as a multi-layered material or a growing plant root. Existing topology simplification methods are designed for single shapes, and applying them independently to shapes in a sequence may lose the nesting property. We formulate the nesting-constrained simplification task as an optimal labelling problem on a set of candidate shape deletions (“cuts”) and additions (“fills”). We explored several optimization strategies, including a greedy heuristic that sequentially propagates labels, a state-space search algorithm that is provably optimal, and a beam-search variant with controllable complexity. Evaluation on synthetic and real-world data shows that our method is as effective as single-shape simplification methods in reducing topological complexity and minimizing geometric changes, and it additionally ensures nesting. Also, the beam-search strategy is found to strike the best balance between optimality and efficiency.
AB - We present a method for removing unwanted topological features (e.g., islands, handles, cavities) from a sequence of shapes where each shape is nested in the next. Such sequences can be found in nature, such as a multi-layered material or a growing plant root. Existing topology simplification methods are designed for single shapes, and applying them independently to shapes in a sequence may lose the nesting property. We formulate the nesting-constrained simplification task as an optimal labelling problem on a set of candidate shape deletions (“cuts”) and additions (“fills”). We explored several optimization strategies, including a greedy heuristic that sequentially propagates labels, a state-space search algorithm that is provably optimal, and a beam-search variant with controllable complexity. Evaluation on synthetic and real-world data shows that our method is as effective as single-shape simplification methods in reducing topological complexity and minimizing geometric changes, and it additionally ensures nesting. Also, the beam-search strategy is found to strike the best balance between optimality and efficiency.
KW - CCS Concepts
KW - Volumetric models
KW - • Computing methodologies → Shape analysis
UR - http://www.scopus.com/inward/record.url?scp=85139529311&partnerID=8YFLogxK
U2 - 10.1111/cgf.14611
DO - 10.1111/cgf.14611
M3 - Article
AN - SCOPUS:85139529311
SN - 0167-7055
VL - 41
SP - 161
EP - 173
JO - Computer Graphics Forum
JF - Computer Graphics Forum
IS - 5
ER -