PartUV: Part-Based UV Unwrapping of 3D Meshes

• URL: http://arxiv.org/abs/2511.16659v1
• Date: Thu, 20 Nov 2025 18:58:39 GMT
• Title: PartUV: Part-Based UV Unwrapping of 3D Meshes
• Abstract summary: PartUV is a part-based UV unwrapping pipeline that generates fewer, part-aligned charts while maintaining low distortion.<n>PartUV is evaluated across four diverse datasets, including man-made, CAD, AI-generated, and Common Shapes.
• Score: 33.100992979838814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: UV unwrapping flattens 3D surfaces to 2D with minimal distortion, often requiring the complex surface to be decomposed into multiple charts. Although extensively studied, existing UV unwrapping methods frequently struggle with AI-generated meshes, which are typically noisy, bumpy, and poorly conditioned. These methods often produce highly fragmented charts and suboptimal boundaries, introducing artifacts and hindering downstream tasks. We introduce PartUV, a part-based UV unwrapping pipeline that generates significantly fewer, part-aligned charts while maintaining low distortion. Built on top of a recent learning-based part decomposition method PartField, PartUV combines high-level semantic part decomposition with novel geometric heuristics in a top-down recursive framework. It ensures each chart's distortion remains below a user-specified threshold while minimizing the total number of charts. The pipeline integrates and extends parameterization and packing algorithms, incorporates dedicated handling of non-manifold and degenerate meshes, and is extensively parallelized for efficiency. Evaluated across four diverse datasets, including man-made, CAD, AI-generated, and Common Shapes, PartUV outperforms existing tools and recent neural methods in chart count and seam length, achieves comparable distortion, exhibits high success rates on challenging meshes, and enables new applications like part-specific multi-tiles packing. Our project page is at https://www.zhaoningwang.com/PartUV.

Related papers

• High-Fidelity Surface Splatting-Based 3D Reconstruction from Multi-View Images [0.0]
  Implicit Moving Least Squares (IMLS) enables direct conversion of point clouds into signed distance and texture fields.<n>We introduce a compact kernel with local support and greater flexibility, allowing better control over frequency content.<n> Experiments show state-of-the-art performance in both surface reconstruction and rendering, yielding more accurate geometry and sharper visuals.
  arXiv  Detail & Related papers  (2026-05-08T05:23:28Z)
• HBSplat: Robust Sparse-View Gaussian Reconstruction with Hybrid-Loss Guided Depth and Bidirectional Warping [11.035994094874141]
  HBSplat is a framework that seamlessly integrates robust structural cues, virtual view constraints, and occluded region completion.<n> HBSplat sets a new state-of-the-art, achieving up to 21.13 dB PSNR and 0.189 LPIPS, while maintaining real-time inference.
  arXiv  Detail & Related papers  (2025-09-29T15:03:31Z)
• SeqTex: Generate Mesh Textures in Video Sequence [62.766839821764144]
  We introduce SeqTex, a novel end-to-end framework for training 3D texture generative models.<n>We show that SeqTex achieves state-of-the-art performance on both image-conditioned and text-conditioned 3D texture generation tasks.
  arXiv  Detail & Related papers  (2025-07-06T07:58:36Z)
• Auto-Regressive Surface Cutting [16.593710516134472]
  We introduce SeamGPT, an auto-recanned model that generates cutting seams by mimicking professional models.<n>Our approach achieves exceptional performance on UV unwrapping benchmarks containing both manifold and non-manifold meshes.<n>It enhances existing 3D segmentation tools by providing clean boundaries for part decomposition.
  arXiv  Detail & Related papers  (2025-06-22T12:53:07Z)
• SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling [79.56581753856452]
  SparseFlex is a novel sparse-structured isosurface representation that enables differentiable mesh reconstruction at resolutions up to $10243$ directly from rendering losses.<n>By enabling high-resolution, differentiable mesh reconstruction and generation with rendering losses, SparseFlex significantly advances the state-of-the-art in 3D shape representation and modeling.
  arXiv  Detail & Related papers  (2025-03-27T17:46:42Z)
• Volumetric Surfaces: Representing Fuzzy Geometries with Layered Meshes [59.17785932398617]
  High-quality view synthesis relies on volume rendering, splatting, or surface rendering.<n>We present a novel representation for real-time view synthesis where the number of sampling locations is small and bounded.<n>We achieve this by representing objects as semi-transparent multi-layer meshes rendered in a fixed order.
  arXiv  Detail & Related papers  (2024-09-04T07:18:26Z)
• Semantic UV mapping to improve texture inpainting for indoor scenes [0.0]
  This work aims to improve texture inpainting after clutter removal in scanned indoor meshes. This is achieved with a new UV mapping pre-processing step which leverages semantic information of indoor scenes to more accurately match the UV islands with the 3D representation of distinct structural elements like walls and floors.
  arXiv  Detail & Related papers  (2024-07-12T13:21:25Z)
• CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
  We propose a novel approach to improve NeRF's performance with sparse inputs. We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
  arXiv  Detail & Related papers  (2024-03-25T15:56:17Z)
• Nuvo: Neural UV Mapping for Unruly 3D Representations [61.87715912587394]
  Existing UV mapping algorithms operate on geometry produced by state-of-the-art 3D reconstruction and generation techniques. We present a UV mapping method designed to operate on geometry produced by 3D reconstruction and generation techniques.
  arXiv  Detail & Related papers  (2023-12-11T18:58:38Z)
• Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering [84.37776381343662]
  Mip-NeRF proposes a multiscale representation as a conical frustum to encode scale information. We propose mip voxel grids (Mip-VoG), an explicit multiscale representation for real-time anti-aliasing rendering. Our approach is the first to offer multiscale training and real-time anti-aliasing rendering simultaneously.
  arXiv  Detail & Related papers  (2023-04-20T04:05:22Z)

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.