mesh repair

The absence of formal verification methods for G-code linear motion in 3D printing hinders assurance of geometric-code consistency. Method: This paper proposes a dimension-elevated semantic representation framework that parses G-code into sets of axis-aligned bounding boxes and their approximated point clouds, integrating geometric modeling with program analysis to enable invariant checking and differential testing across the manufacturing pipeline. Contribution/Results: The framework supports, for the first time, quantitative cross-slicer comparison (Cura vs. PrusaSlicer), error localization, and root-cause analysis of defects introduced during mesh repair (e.g., in MeshLab or Meshmixer). Evaluated on 58 real-world models, it efficiently detects slicing anomalies induced by small geometric features, exposes behavioral discrepancies among mainstream slicers, and identifies new errors inadvertently introduced during repair—thereby significantly enhancing the verifiability and reliability of end-to-end additive manufacturing pipelines.

This study addresses the challenges of recovering sparse control cages from dense meshes, including inferring control requirements, missing feature curves, and difficulty in correcting initial results. To overcome these issues, this work proposes a modeler-inspired agent workflow that iteratively optimizes through planning and diagnosis phases. A stateful feedback mechanism is introduced to support repair or rollback decisions by incorporating semantic judgments into the pipeline. To ensure inspectability, the planner avoids directly generating vertices, instead integrating multi-view geometric evidence analysis with tool execution verification. Evaluated on a fixed benchmark queue, the proposed method outperforms automatic remeshing approaches across five metrics, reducing Chamfer-L1 to 0.443% and improving the F-score to 92.78%, thereby achieving highly controllable mesh-to-subdivision surface reconstruction.

This work addresses the common failure of geometric approximation during mesh generation from parametric boundary representation (B-Rep) models, which often corrupts the original topological structure and leads to incorrect adjacency relationships. The paper proposes a topology-first meshing approach that, for the first time, enforces B-Rep topology as a hard constraint at the algorithmic level, rigorously preserving topological invariance throughout the discretization process. Geometric deviation is controlled solely through user-defined tolerances, thereby decoupling topological correctness from geometric approximation. The method produces robust, topologically consistent meshes without requiring post-processing and has been validated on thousands of real-world CAD models—including cases where conventional tools fail—demonstrating significantly improved reliability for downstream applications.

This work addresses the challenge that geometric and topological anomalies—such as self-intersections and edge collapses—in intermediate wireframes often render generated B-Rep models invalid, while retraining large generative models remains prohibitively expensive. To circumvent this, the authors propose the Wireframe Debugging and Repair (WDR) framework, which intervenes at the intermediate wireframe stage without requiring model retraining. WDR first identifies anomalies by combining a vision-language model for coarse filtering with a Geometry-Topology Anomaly Detector (GTAD), then performs test-time optimization via an Energy-Guided Geometry-Topology Repair (EGGTR) module coupled with diffusion-based resampling. This approach establishes the first training-free pipeline for wireframe anomaly detection and repair, significantly improving B-Rep kernel validation success rates while preserving the diversity and distributional fidelity of generated CAD models.

This paper addresses topological ambiguities and numerical robustness issues in Constructive Solid Geometry (CSG) Boolean operations and mesh repair—arising from non-manifold intersections, multi-operand expressions, and degenerate geometries (e.g., coplanar or collinear features). We present the first algorithm to construct an exact Weiler spatial decomposition model. Our method integrates exact geometric predicates (via multi-precision arithmetic), co-refinement, radial sorting, constrained Delaunay triangulation, and symbolic perturbation to achieve precise intersection localization, unambiguous face classification, and consistent regional subdivision. Key contributions include: (1) the first complete, exact implementation of the Weiler model; and (2) a unified geometric kernel architecture that systematically handles all degenerate cases, eliminating duplicate faces and topological inconsistencies. Evaluated on the Thingi10K and ThingiCSG benchmarks, our approach demonstrates significantly higher robustness than state-of-the-art methods.

研究提出激光扫描和图像拓扑优化两种非接触方法，用于检测和量化结构件的表面及次表面缺陷。

This work addresses the challenge of watertight remeshing for meshes with complex topology, single-layer structures, or large missing regions, where existing methods often fail to infer globally consistent inside–outside partitions from local geometry, leading to volume-inconsistent pseudo-watertight reconstructions such as double-shell artifacts. We propose the first approach that rigorously formulates watertight remeshing as a voxel labeling problem, building a binary labeling model over a Delaunay tetrahedralization and achieving globally consistent watertight surface reconstruction through graph-cut energy minimization with one-sided constraints. The method inherently guarantees watertightness, effectively suppresses double-shell artifacts, and eliminates unsupported boundaries via a weighted interface penalty term. Extensive evaluations on CelloScan, CelloFill, and ModelNet10 benchmarks demonstrate significant improvements over state-of-the-art methods, particularly yielding compact and volume-consistent solid reconstructions for complex topologies and single-layer geometries.

This study addresses the challenge of precisely repairing SVG code under visual instruction guidance, requiring models to modify only specified regions while preserving all other protected content. To this end, the authors introduce a benchmark comprising 40 high-difficulty tasks and propose a novel dual-norm reward mechanism that integrates semantic invariance with attribute-aware tolerance. They also introduce new evaluation dimensions, including validity-gated repair progress and Unintended Change Rate (UCR). Through rigorous assessment involving SVG parsing-rendering validation, structural-semantic consistency checks, and deterministic norm scoring across 34 models, they find that even the strongest model achieves a full-norm success rate of merely 15.0%, with an average repair progress of 43.7%, revealing significant limitations in current approaches to faithful, constrained editing.

This study addresses the reliance on manual intervention for seam planning in production-level automatic UV unwrapping of quadrilateral meshes. We propose a training-free agent-based method that leverages vision-language models (VLMs) integrated with domain knowledge. By employing query-based mesh representations and a domain-specific language (DSL), our approach decouples high-level intent planning from low-level edge selection, while introducing a feedback loop mechanism to iteratively refine seams. This design ensures compatibility across backend VLMs and enables scalability to extremely large meshes. Experimental results demonstrate that the proposed method reduces the number of charts by 2.9× and shortens seam length by 1.63×, achieving an 80.9% preference rate among professional artists.

为解决生成式3D模型几何精度不足的问题，InstructMesh通过区域选择和针对性操作提供交互式修复工具，用户可通过自然语言或滑块控制进行编辑。