Volume 44 (2025)

Permanent URI for this community

https://diglib.eg.org/handle/10.2312/3607127

Browse

Browsing Volume 44 (2025) by Subject "Applied computing → Computer"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Atomizer: Beyond Non-Planar Slicing for Fused Filament Fabrication
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Chermain, Xavier; Cocco, Giovanni; Zanni, Cédric; Garner, Eric; Hugron, Pierre-Alexandre; Lefebvre, Sylvain; Attene, Marco; Sellán, Silvia
    Fused filament fabrication (FFF) enables users to quickly design and fabricate parts with unprecedented geometric complexity, fine-tuning both the structural and aesthetic properties of each object. Nevertheless, the full potential of this technology has yet to be realized, as current slicing methods fail to fully exploit the deposition freedom offered by modern 3D printers. In this work, we introduce a novel approach to toolpath generation that moves beyond the traditional layer-based concept. We use frames, referred to as atoms, as solid elements instead of slices. We optimize the distribution of atoms within the part volume to ensure even spacing and smooth orientation while accurately capturing the part's geometry. Although these atoms collectively represent the complete object, they do not inherently define a fabrication plan. To address this, we compute an extrusion toolpath as an ordered sequence of atoms that, when followed, provides a collision-free fabrication strategy. This general approach is robust, requires minimal user intervention compared to existing techniques, and integrates many of the best features into a unified framework: precise deposition conforming to non-planar surfaces, effective filling of narrow features - down to a single path - and the capability to locally print vertical structures before transitioning elsewhere. Additionally, it enables entirely new capabilities, such as anisotropic appearance fabrication on curved surfaces.
  • Thumbnail Image
    Item
    Exact and Efficient Mesh-Kernel Generation
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Nehring-Wirxel, Julius; Kern, Paul; Trettner, Philip; Kobbelt, Leif; Attene, Marco; Sellán, Silvia
    The mesh kernel for a star-shaped mesh is a convex polyhedron given by the intersection of all half-spaces defined by the faces of the input mesh. For all non-star-shaped meshes, the kernel is empty. We present a method to robustly and efficiently compute the kernel of an input triangle mesh by using exact plane-based integer arithmetic to compute the mesh kernel. We make use of several ways to accelerate the computation time. Since many applications just require information if a non-empty mesh kernel exists, we also propose a method to efficiently determine whether a kernel exists by developing an exact plane-based linear program solver. We evaluate our method on a large dataset of triangle meshes and show that in contrast to previous methods, our approach is exact and robust while maintaining a high performance. It is on average two orders of magnitude faster than other exact state-of-the-art methods and often about one order of magnitude faster than non-exact methods.
  • Thumbnail Image
    Item
    A Solver-Aided Hierarchical Language for LLM-Driven CAD Design
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Jones, Ben T.; Zhang, Zihan; Hähnlein, Felix; Matusik, Wojciech; Ahmad, Maaz; Kim, Vladimir; Schulz, Adriana; Christie, Marc; Pietroni, Nico; Wang, Yu-Shuen
    Parametric CAD systems use domain-specific languages (DSLs) to represent geometry as programs, enabling both flexible modeling and structured editing. With the rise of large language models (LLMs), there is growing interest in generating such programs from natural language. This raises a key question: what kind of DSL best supports both CAD generation and editing, whether performed by a human or an AI? In this work, we introduce AIDL, a hierarchical, solver-aided DSL designed to align with the strengths of LLMs while remaining interpretable and editable by humans. AIDL enables high-level reasoning by breaking problems into abstract components and structural relationships, while offloading low-level geometric reasoning to a constraint solver. We evaluate AIDL in a 2D text-to-CAD setting using a zero-shot prompt-based interface and compare it to OpenSCAD, a widely used CAD DSL that appears in LLM training data. AIDL produces results that are visually competitive and significantly easier to edit. Our findings suggest that language design is a powerful complement to model training and prompt engineering for building collaborative AI-human tools in CAD. Code is available at https://github.com/deGravity/aidl.