WildFab: Multi-Axis 3D Printing from Models in the Wild

• URL: http://arxiv.org/abs/2609.02413v1
• Date: Wed, 02 Sep 2026 10:35:32 GMT
• Title: WildFab: Multi-Axis 3D Printing from Models in the Wild
• Abstract summary: WildFab is a computational framework for multi-axis 3D printing.<n>It computes spatial toolpath and global collision-free motion from input models.
• Score: 4.753066305078767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-axis 3D printing enables support-free fabrication and improved part quality, but robustly processing real-world geometries remains challenging. Models from design workflows or direct data acquisition often contain solid--shell combinations and non-manifold structures. Handling such models in the wild typically requires time-consuming geometry repair, which may alter the intended geometry. In this work, we present WildFab, a computational framework for multi-axis 3D printing that directly computes spatial toolpath and global collision-free motion from input models. Our pipeline builds on a hybrid query representation that combines a neural unsigned distance field (UDF) with a regularized generalized winding number field (reg-GWN). The UDF supplies differentiable surface-distance and direction queries, while the reg-GWN resolves near-surface ambiguity in the fitted UDF by providing reliable surface localization and a solid-void indicator. Based on this representation, we introduce a high-precision spatial toolpath computation algorithm that iteratively projects points between optimized guidance-field level sets and reg-GWN gradient-magnitude ridges. Subsequently, we develop an efficient and robust coarse-to-fine collision checking scheme for motion planning: UDF-based rejection first identifies potential collisions, while time-varying reg-GWN verification accurately resolves collision pairs for both solid and shell components. We validate WildFab on diverse inputs, demonstrating successful computation from non-manifold parametric surfaces, voxelized topology-optimization results, implicit models, raw scanned point clouds, and non-watertight meshes. The fabrication results highlight our method's ability to advance end-to-end design-to-3DP workflows.

Related papers

• RegFormer++: An Efficient Large-Scale 3D LiDAR Point Registration Network with Projection-Aware 2D Transformer [71.59462491691819]
  We propose a novel end-to-end differential transformer network, termed RegFormer++, for large-scale point cloud alignment.<n>Because we fill original 3D coordinates into 2D projected positions, our designed transformer can benefit from both high efficiency in 2D processing and accuracy from 3D geometric information.<n>Experiments on KITTI, NuScenes, and Argoverse datasets demonstrate that our model achieves state-of-the-art performance in terms of both accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-03-15T09:03:10Z)
• Tail-Aware Post-Training Quantization for 3D Geometry Models [58.79500829118265]
  Post-Training Quantization (PTQ) enables efficient inference without retraining.<n>PTQ fails to transfer effectively to 3D models due to intricate feature distributions and prohibitive calibration overhead.<n>We propose TAPTQ, a Tail-Aware Post-Training Quantization pipeline for 3D geometric learning.
  arXiv  Detail & Related papers  (2026-02-02T07:21:15Z)
• BladeSDF : Unconditional and Conditional Generative Modeling of Representative Blade Geometries Using Signed Distance Functions [4.188806282965805]
  This paper introduces a domain-specific implicit generative framework for turbine blade geometry using DeepSDF.<n>By integrating constraints, objectives, and performance metrics, this approach advances beyond traditional 2D-guided or unconstrained 3D pipelines.
  arXiv  Detail & Related papers  (2026-01-19T23:02:33Z)
• LoG3D: Ultra-High-Resolution 3D Shape Modeling via Local-to-Global Partitioning [26.88556500272625]
  We propose a novel 3D variational autoencoder framework built upon unsigned distance fields (UDFs)<n>Our core innovation is a local-to-global architecture that processes the UDF by partitioning it into uniform subvolumes, UBlocks.<n> Experiments demonstrate state-of-the-art performance in both reconstruction accuracy and generative quality, yielding superior surface smoothness and geometric flexibility.
  arXiv  Detail & Related papers  (2025-11-13T07:34:43Z)
• ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing [0.4666493857924357]
  This work introduces ShapeGen3DCP, a deep learning framework for prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP)<n>The framework is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes.<n>The training dataset was synthetically generated using a well-established Particle Finite Element (PFEM) model of 3DCP, overcoming the scarcity of experimental data.
  arXiv  Detail & Related papers  (2025-10-02T13:30:20Z)
• Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis [51.37795317716487]
  We propose the Adaptive Point-Prompt Tuning (APPT) method, which fine-tunes pre-trained models with a modest number of parameters.<n>We convert raw point clouds into point embeddings by aggregating local geometry to capture spatial features followed by linear layers.<n>To calibrate self-attention across source domains of any modality to 3D, we introduce a prompt generator that shares weights with the point embedding module.
  arXiv  Detail & Related papers  (2025-08-30T06:02:21Z)
• VortSDF: 3D Modeling with Centroidal Voronoi Tesselation on Signed Distance Field [5.573454319150408]
  We introduce a volumetric optimization framework that combines explicit SDF fields with a shallow color network, in order to estimate 3D shape properties over tetrahedral grids. Experimental results with Chamfer statistics validate this approach with unprecedented reconstruction quality on various scenarios such as objects, open scenes or human.
  arXiv  Detail & Related papers  (2024-07-29T09:46:39Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• Positional Encoding Augmented GAN for the Assessment of Wind Flow for Pedestrian Comfort in Urban Areas [0.41998444721319217]
  This work rephrases the problem from computing 3D flow fields using CFD to a 2D image-to-image translation-based problem on the building footprints to predict the flow field at pedestrian height level. We investigate the use of generative adversarial networks (GAN), such as Pix2Pix and CycleGAN representing state-of-the-art for image-to-image translation task in various domains.
  arXiv  Detail & Related papers  (2021-12-15T19:37:11Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)
• PnP-DETR: Towards Efficient Visual Analysis with Transformers [146.55679348493587]
  Recently, DETR pioneered the solution vision tasks with transformers, it directly translates the image feature map into the object result. Recent transformer-based image recognition model andTT show consistent efficiency gain.
  arXiv  Detail & Related papers  (2021-09-15T01:10:30Z)

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.