Optimizing 4D Wires for Sparse 3D Abstraction
- URL: http://arxiv.org/abs/2605.11977v1
- Date: Tue, 12 May 2026 11:33:43 GMT
- Title: Optimizing 4D Wires for Sparse 3D Abstraction
- Abstract summary: We present a unified framework for 3D geometric abstraction using a single continuous 4D wire.<n>We show that our unified representation produces structures with higher fidelity and improved structural coherence.
- Score: 12.365535344035903
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a unified framework for 3D geometric abstraction using a single continuous 4D wire, parameterized as a B-spline with spatial coordinates and variable width $(x,y,z,w)$. Existing approaches typically represent shapes as collections of many independent curve segments, which often leads to fragmented structures and limited physical realizability. In contrast, we show that a single continuous spline is sufficiently expressive to capture complex volumetric forms while enforcing global topological coherence. By imposing continuity, our method transforms 3D sketching from a local density-accumulation process into a global routing problem, providing a strong inductive bias toward cleaner aesthetics and improved structural coherence. To enable gradient-based optimization, we introduce a differentiable rendering pipeline that efficiently rasterizes variable-width curves with bounded projection error. This formulation supports robust optimization using modern guidance signals such as Score Distillation Sampling (SDS) or CLIP. We demonstrate applications including image-to-3D abstraction, multi-view wire art generation, and differentiable stylized surface filling. Experiments show that our unified representation produces structures with higher semantic fidelity and improved structural coherence compared to approaches based on collections of discrete curves.
Related papers
- Inverse Rendering for Modeling with Line Primitives [22.068172674478078]
We present an inverse rendering method for reconstructing fuzzy geometry using explicit line segments.<n>The resulting representation integrates seamlessly with standard graphics pipelines, enabling cross-platform rendering, various shading models, and physical simulation.
arXiv Detail & Related papers (2026-09-01T03:05:52Z) - PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation [105.86385617182879]
State-of-the-art single-image 3D reconstruction methods often rely on complex hybrid architectures and loss functions.<n>We show that such architectural overhead and intricate loss formulations are unnecessary.<n>We introduce a minimalist pixel-space Diffusion Transformer, built on a plain ViT, that operates directly on raw 3D point map patches.
arXiv Detail & Related papers (2026-07-02T17:59:56Z) - Capacity-Controlled Multi-View Stylization of 3D Gaussian Splatting [66.3033154284523]
We propose a capacity-controlled framework for multi-view stylization of 3DGS.<n>By introducing explicit column-capacity constraints with tunable strength, our formulation mitigates many-to-one matching.<n>We also incorporate a novel cross-view matching guidance to constrain correspondences between scene content and style patterns.
arXiv Detail & Related papers (2026-06-25T08:36:50Z) - Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers [46.87466345672103]
Boundary representation (B-rep) is the industry standard for computer-aided design (CAD)<n>While deep learning shows promise in processing B-rep models, existing methods suffer from a representation gap.<n>We introduce Brep2Shape, a novel self-supervised pre-training method designed to align abstract boundary representations with intuitive shape representations.
arXiv Detail & Related papers (2026-02-07T08:00:47Z) - ATATA: One Algorithm to Align Them All [74.76451498236437]
We suggest a new multi-modal algorithm for joint inference of paired structurally aligned samples with Rectified Flow models.<n>We show the applicability of our method to the domains of image, video, and 3D shape generation using state-of-the-art baselines and evaluate it against both editing-based and joint inference-based competing approaches.
arXiv Detail & Related papers (2026-01-16T11:11:33Z) - Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding [86.55824709875598]
We propose a joint enhancement framework for 3D semantic Gaussian modeling that synergizes both semantic and rendering branches.<n>Unlike conventional point cloud shape encoding, we introduce an anisotropic 3D Gaussian Chebyshev descriptor to capture fine-grained 3D shape details.<n>We employ a cross-scene knowledge transfer module to continuously update learned shape patterns, enabling faster convergence and robust representations.
arXiv Detail & Related papers (2026-01-05T18:33:50Z) - RiemanLine: Riemannian Manifold Representation of 3D Lines for Factor Graph Optimization [49.83974390433746]
This paper introduces textbfRiemanLine, a unified minimal representation for 3D lines.<n>Our key idea is to decouple each line landmark into global and local components.<n>Experiments on ICL-NUIM, TartanAir, and synthetic benchmarks demonstrate that our method achieves significantly more accurate pose estimation and line reconstruction.
arXiv Detail & Related papers (2025-08-06T11:27:38Z) - Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstruction [14.628742412460346]
This paper presents an end-to-end framework for reconstructing 3D parametric curves directly from multi-view edge maps.<n>We propose a novel bi-directional coupling mechanism between parametric curves and edge-oriented Gaussian components.<n>Our method significantly reduces the parameter count during training, achieving both higher efficiency and superior performance compared to existing approaches.
arXiv Detail & Related papers (2025-06-26T15:48:08Z) - WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction [13.645442589551354]
WIR3D is a technique for abstracting 3D shapes through a sparse set of visually meaningful curves in 3D.<n>We optimize the parameters of Bezier curves such that they faithfully represent both the geometry and salient visual features.<n>We successfully apply our method for shape abstraction over a broad dataset of shapes.
arXiv Detail & Related papers (2025-05-07T21:28:05Z) - CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generation [11.447223770747051]
CLR ContinuousWire encodes curves as Parametric Curves along with their Parametric Curves into a continuous and fixed latent space.<n>This unified approach generates both geometry and topology.
arXiv Detail & Related papers (2025-04-27T09:32:42Z) - LinPrim: Linear Primitives for Differentiable Volumetric Rendering [51.56484100374058]
We introduce two new scene representations based on linear primitives.<n>We present a different octaiableizer that runs efficiently on GPU.<n>We demonstrate comparable performance to state-of-the-art methods.
arXiv Detail & Related papers (2025-01-27T18:49:38Z)
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.