Displaced signed distance fields for additive manufacturing
Alan Brunton, Lubna Abu Rmaileh
Abstract
We propose displaced signed distance fields, an implicit shape representation to accurately, efficiently and robustly 3D-print finely detailed and smoothly curved surfaces at native device resolution. As the resolution and accuracy of 3D printers increase, accurate reproduction of such surfaces becomes increasingly realizable from a hardware perspective. However, representing such surfaces with polygonal meshes requires high polygon counts, resulting in excessive storage, transmission and processing costs. These costs increase with print size, and can become exorbitant for large prints. Our implicit formulation simultaneously allows the augmentation of low-polygon meshes with compact meso-scale topographic information, such as displacement maps, and the realization of curved polygons, while leveraging efficient, streaming-compatible, discrete voxel-wise algorithms. Critical for this is careful treatment of the input primitives, their voxel approximation and the displacement to the true surface. We further propose a robust sign estimation to allow for incomplete, non-manifold input, whether human-made for onscreen rendering or directly out of a scanning pipeline. Our framework is efficient both in terms of time and space. The running time is independent of the number of input polygons, the amount of displacement, and is constant per voxel. The storage costs grow sub-linearly with the number of voxels, making our approach suitable for large prints. We evaluate our approach for efficiency and robustness, and show its advantages over standard techniques.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext e725535b-d0a3-43c8-b882-4422e9d99f9fCited by top-tier papers4
- A Heat Method for Generalized Signed DistanceNicole Feng, Keenan CraneSIGGRAPH 2024 · 26 citations
- Reach for the Arcs: Reconstructing Surfaces from SDFs via Tangent PointsSilvia Sellán, Yingying Ren, Christopher Batty, Oded SteinSIGGRAPH 2024 · 10 citations
- Color Matching and Biomimicry for Multi-Material Dental 3D PrintingAndrás Simon, Danwu Chen, Philipp Urban, Vincent Duveiller et al.SIGGRAPH 2025 · 1 citation
- Dual Contouring of Signed Distance DataXiana Carrera, Ningna Wang, Christopher Batty, Oded Stein et al.SIGGRAPH 2026 · 1 citation
Builds on4
- Implicit Neural Representations with Periodic Activation FunctionsVincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell et al.NeurIPS 2020 · 4,008 citations
- Implicit Geometric Regularization for Learning ShapesAmos Gropp, Lior Yariv, Niv Haim, Matan Atzmon et al.ICML 2020 · 1,001 citations
- SALD: Sign Agnostic Learning with DerivativesMatan Atzmon, Yaron LipmanICLR 2021 · 5 citations
- SAL: Sign Agnostic Learning of Shapes From Raw DataMatan Atzmon, Yaron LipmanCVPR 2020
Related papers
- Neural Geometric Level of Detail: Real-Time Rendering With Implicit 3D ShapesTowaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis et al.CVPR 2021
- Shape dithering for 3D printingMostafa Morsy Abdelkader Morsy, Alan Brunton, Philipp UrbanSIGGRAPH 2022 · 9 citations
- Marching-Primitives: Shape Abstraction from Signed Distance FunctionWeixiao Liu, Yuwei Wu, Sipu Ruan, Gregory S. ChirikjianCVPR 2023
- SuperSDF: Sparse SDF Super-Resolution for Surface ExtractionSagar Panwar, Nissim Maruani, Céline Loscos, Mathieu Desbrun et al.SIGGRAPH 2026
- Gradient-SDF: A Semi-Implicit Surface Representation for 3D ReconstructionChristiane Sommer, Lu Sang, David Schubert, Daniel CremersCVPR 2022 · 24 citations
