Sketch2MinSurf: Vision-Language Guided Generation of Editable Minimal Surfaces from Hand-Drawn Sketches
- URL: http://arxiv.org/abs/2605.20733v1
- Date: Wed, 20 May 2026 05:37:31 GMT
- Title: Sketch2MinSurf: Vision-Language Guided Generation of Editable Minimal Surfaces from Hand-Drawn Sketches
- Abstract summary: We present Sketch2MinSurf, a hybrid vision-language and geometric framework to generate 3D surfaces from hand-drawn sketches.<n>On a test set of 100 sketches, Sketch2MinSurf achieves a topological similarity score of 0.844, outperforming existing sketch-to-shape baselines.
- Score: 19.571482380413794
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Converting hand-drawn sketches into structured 3D geometries remains challenging due to the difficulty of representing non-Euclidean surfaces and maintaining topological consistency. Existing generative models such as GANs, NeRFs, and diffusion architectures often fail to produce editable manifolds directly usable in downstream design workflows. We present Sketch2MinSurf, a hybrid vision-language and geometric optimization framework that integrates vision-language guidance with minimal-surface theory to generate smooth and editable 3D surfaces from hand-drawn sketches. The core of our approach is a spatial-topological encoding that represents geometry as tuples of node coordinates and real/virtual edge skeletons, enabling stable topological control during generation. We further introduce the Sketch2MinSurf Structural Loss (S2MS-Loss), a reward-modulated objective that jointly constrains geometric reconstruction and topological coherence. On a test set of 100 sketches, Sketch2MinSurf achieves a topological similarity score of 0.844, outperforming existing sketch-to-shape baselines. The generated manifolds are directly editable and free from non-manifold artifacts. A public art installation at a university showcases the method's potential for human-intent-driven 3D form generation. The dataset and code are available at https://anonymous.4open.science/r/Sketch2MinSurf/.
Related papers
- Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction [56.51413081953813]
SurfSVR is a novel sparse voxel reconstruction paradigm that treats 2D surface priors as explicit 3D geometric regularizers.<n>It consistently improves sparse voxel reconstruction across scenes with substantially different visibility and geometry characteristics.
arXiv Detail & Related papers (2026-08-12T11:25:31Z) - TopoGS: Planar Reconstruction via Topology-aware 3D Gaussian Splatting [15.690820181002517]
We introduce TopoGS, the first 3DGS framework to explicitly integrate both planar and topological constraints for coherent 3D reconstruction.<n>Specifically, we extract global 2D topological relationships from multi-view image segmentations and anchor Gaussian primitives to these structural elements.<n>By enforcing strict multi-view consistency alongside these topological constraints, our method significantly mitigates geometric misalignments and produces connected, structured 3D models.
arXiv Detail & Related papers (2026-07-18T14:27:49Z) - Spherical Geometry Diffusion: Generating High-quality 3D Face Geometry via Sphere-anchored Representations [18.442834011472005]
A fundamental challenge in text-to-3D face generation is achieving high-quality geometry.<n>We introduce the Spherical Geometry Representation, a novel face representation that anchors geometric signals to uniform spherical coordinates.<n>We then introduce Spherical Diffusion Geometry, a conditional diffusion framework built upon this 2D map.
arXiv Detail & Related papers (2026-01-19T20:15:45Z) - sketch2symm: Symmetry-aware sketch-to-shape generation via semantic bridging [12.390999095290404]
We propose Sketch2Symm, a two-stage generation method that produces geometrically consistent 3D from sketches.<n>Our approach introduces semantic bridging via sketch-to-image translation to enrich sparse sketch representations.<n>Our method achieves superior performance compared to existing sketch-based reconstruction methods in terms of Chamfer Distance, Earth Mover's Distance, and F-Score.
arXiv Detail & Related papers (2025-10-13T11:49:45Z) - Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification [59.17489431187807]
We propose a framework that enhances 3D geometric fidelity by leveraging CLIP's hierarchical spatial semantics.<n>Our method significantly improves 3D few-shot class-incremental learning, achieving superior geometric coherence and robustness to texture bias.
arXiv Detail & Related papers (2025-09-18T13:45:08Z) - Shape from Semantics: 3D Shape Generation from Multi-View Semantics [30.969299308083723]
Existing 3D reconstruction methods utilize guidances such as 2D images, 3D point clouds, shape contours and single semantics to recover the 3D surface.<n>We propose a novel 3D modeling task called Shape from Semantics'', which aims to create 3D models whose geometry and appearance are consistent with the given text semantics when viewed from different views.
arXiv Detail & Related papers (2025-02-01T07:51:59Z) - Ghost on the Shell: An Expressive Representation of General 3D Shapes [97.76840585617907]
Meshes are appealing since they enable fast physics-based rendering with realistic material and lighting.
Recent work on reconstructing and statistically modeling 3D shapes has critiqued meshes as being topologically inflexible.
We parameterize open surfaces by defining a manifold signed distance field on watertight surfaces.
G-Shell achieves state-of-the-art performance on non-watertight mesh reconstruction and generation tasks.
arXiv Detail & Related papers (2023-10-23T17:59:52Z) - Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud
Analysis [66.49788145564004]
We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology.
Our methods perform favorably against the current state-of-the-art competitors.
arXiv Detail & Related papers (2022-12-17T15:05:25Z) - Neural Convolutional Surfaces [59.172308741945336]
This work is concerned with a representation of shapes that disentangles fine, local and possibly repeating geometry, from global, coarse structures.
We show that this approach achieves better neural shape compression than the state of the art, as well as enabling manipulation and transfer of shape details.
arXiv Detail & Related papers (2022-04-05T15:40:11Z) - Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D
Shape Synthesis [90.26556260531707]
DMTet is a conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels.
Unlike deep 3D generative models that directly generate explicit representations such as meshes, our model can synthesize shapes with arbitrary topology.
arXiv Detail & Related papers (2021-11-08T05:29:35Z) - Deep Geometric Texture Synthesis [83.9404865744028]
We propose a novel framework for synthesizing geometric textures.
It learns texture statistics from local neighborhoods of a single reference 3D model.
Our network displaces mesh vertices in any direction, enabling synthesis of geometric textures.
arXiv Detail & Related papers (2020-06-30T19:36:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.