Evocube: A Genetic Labelling Framework for Polycube‐Maps

Corentin, Dumery,; Protais, François; Mestrallet, S.; Bourcier, Camille H; Ledoux, Franck

doi:10.1111/cgf.14649

Cited by 4 publications

(2 citation statements)

References 54 publications

(87 reference statements)

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“…The most widely‐studied fully automatic approaches explored by the computer graphics community in recent years employ adaptive grids [GSP19,PLC*21,LPC22,Mar09], polycubes [XGDC17, GSZ11, LVS*13, FXB16, FBL16, CLS16, MCBC22, DPM*22, GLYL20, HJS*14], or frame fields [NRP11, LLX*12, JHW*13, LZC*18, KLF16, SVB17, CC19, PBS20, BBC22]. Grid‐based approaches are versatile and unconditionally robust, but lack fine control on mesh quality and are prone to creating meshes with poor geometry and topology [LPC22].…”

Section: Related Workmentioning

confidence: 99%

HexBox: Interactive Box Modeling of Hexahedral Meshes

Zoccheddu,

Gobbetti,

Livesu

et al. 2023

Computer Graphics Forum

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We introduce HexBox, an intuitive modeling method and interactive tool for creating and editing hexahedral meshes. Hexbox brings the major and widely validated surface modeling paradigm of surface box modeling into the world of hex meshing. The main idea is to allow the user to box‐model a volumetric mesh by primarily modifying its surface through a set of topological and geometric operations. We support, in particular, local and global subdivision, various instantiations of extrusion, removal, and cloning of elements, the creation of non‐conformal or conformal grids, as well as shape modifications through vertex positioning, including manual editing, automatic smoothing, or, eventually, projection on an externally‐provided target surface. At the core of the efficient implementation of the method is the coherent maintenance, at all steps, of two parallel data structures: a hexahedral mesh representing the topology and geometry of the currently modeled shape, and a directed acyclic graph that connects operation nodes to the affected mesh hexahedra. Operations are realized by exploiting recent advancements in grid‐based meshing, such as mixing of 3‐refinement, 2‐refinement, and face‐refinement, and using templated topological bridges to enforce on‐the‐fly mesh conformity across pairs of adjacent elements. A direct manipulation user interface lets users control all operations. The effectiveness of our tool, released as open source to the community, is demonstrated by modeling several complex shapes hard to realize with competing tools and techniques.

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Section: Related Workmentioning

confidence: 99%

HexBox: Interactive Box Modeling of Hexahedral Meshes

Zoccheddu,

Gobbetti,

Livesu

et al. 2023

Computer Graphics Forum

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“…In hexahedral mesh generation, these spaces are used to generate a conforming all‐hex mesh connectivity that is then projected inside a target shape through an injective map. Both polycube [LVS*13,CLS16,GSZ11,LZS*21,FBL16, GLYL20,FXBH16,PRR*22,DPM*22,MCBC22,HJS*14], frame‐field [LLX*12, BBC22, NRP11, PBS20, LZC*18, KLF16, SVB17, CC19, RSR*18, SRUL16] and skeleton‐based methods [LAPS17, MCK08, LZLW15] operate on grid spaces of this kind. In the last 10 years there has been a lot of research on this topic in the graphics community, but these technologies still struggle to transition to industry because of their lack of robustness, which largely depends from a lack of injectivity of the underlying maps [PCS*22](§8.2).…”

Section: Field Overviewmentioning

confidence: 99%

VOLMAP: a Large Scale Benchmark for Volume Mappings to Simple Base Domains

Cherchi

Livesu

2023

Computer Graphics Forum

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Correspondences between geometric domains (mappings) are ubiquitous in computer graphics and engineering, both for a variety of downstream applications and as core building blocks for higher level algorithms. In particular, mapping a shape to a convex or star‐shaped domain with simple geometry is a fundamental module in existing pipelines for mesh generation, solid texturing, generation of shape correspondences, advanced manufacturing etc. For the case of surfaces, computing such a mapping with guarantees of injectivity is a solved problem. Conversely, robust algorithms for the generation of injective volume mappings to simple polytopes are yet to be found, making this a fundamental open problem in volume mesh processing. VOLMAP is a large scale benchmark aimed to support ongoing research in volume mapping algorithms. The dataset contains 4.7K tetrahedral meshes, whose boundary vertices are mapped to a variety of simple domains, either convex or star‐shaped. This data constitutes the input for candidate algorithms, which are then required to position interior vertices in the domain to obtain a volume map. Overall, this yields more than 22K alternative test cases. VOLMAP also comprises tools to process this data, analyze the resulting maps, and extend the dataset with new meshes, boundary maps and base domains. This article provides a brief overview of the field, discussing its importance and the lack of effective techniques. We then introduce both the dataset and its major features. An example of comparative analysis between two existing methods is also present.

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