3D Shape Matching

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19 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Blueprint2CAD: Benchmarking Foundation Models on Engineering Drawing-to-CAD Render Correspondence
Nov 2, 2026Alexander Valverde, Danny Xie-Li, Zhichao Wang +1VLM Evaluation Cross-View Image Matching
Novineer · Instituto Tecnológico de Costa Rica · University of Würzburg
Fast rigid rotational alignment of voxelized 3-D density maps in sliced Wasserstein distance
Oct 29, 2026Oscar Mickelin, Yunpeng Shi, Amit Singer +13D Shape Matching Optimal Transport
Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China · Department of Mathematics, University of California at Davis, Davis CA, USA · Department of Mathematics, Princeton University, Princeton, NJ, USA +3
TokenMatch: 3D Mesh Correspondence Transformer with Curvature-Guided Tokenisation
Sep 3, 2026Adeela Islam, Zorah Lähner, Vittorio Murino +13D ViTs Non-Rigid Shape Correspondence
1Italian Institute of Technology · University of Genoa · 6Max Planck Institute for Informatics, SIC +4
Hyper-Network Neural Functional Maps for Unsupervised Robust 3D Shape Matching
Jun 29, 2026Dongliang Cao, Florian BernardFunctional Maps Non-Rigid Shape Correspondence
University of Bonn, Germany
Articulating then Matching: Zero-Shot Shape Matching for Uncurated Data
Jun 28, 2026Qilong Liu, Qinfeng Xiao, Chenyuan Yi +2Non-Rigid Shape Correspondence 3D Shape Matching
Hong Kong Polytechnic University, HK SAR · Artificial Intelligence in Design, HK SAR
Coarse-to-Fine: A Hybrid Self-Supervised Method for Non-rigid 3D Shape Matching
Jun 25, 2026Feifan Luo, Ting Li, Zhao Li +1Self-Supervised Learning Functional Maps
Zhejiang University, China · Zhejiang Lab, China · Wenzhou University of Technology, China
Best Segmentation Buddies for Image-Shape Correspondence
May 18, 2026Itai Lang, Dongwei Lyu, Dale Decatur +13D Semantic Segmentation 3D Shape Matching
University of Chicago
SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals
May 18, 2026Soyeon Yoon, Chang Wook Seo, Hyunjung ShimGeometric Representation Learning Non-Rigid Shape Correspondence
1KAIST AI · 2Anigma Technologies
ShapeGen: Robotic Data Generation for Category-Level Manipulation
Apr 16, 2026Yirui Wang, Xiuwei Xu, Angyuan Ma +3Robotic Data Collection Generalization in Robotic Manipulation
Tsinghua University
DeepShapeMatchingKit: Accelerated Functional Map Solver and Shape Matching Pipelines Revisited
Apr 11, 2026Yizheng Xie, Lennart Bastian, Congyue Deng +3Functional Maps Non-Rigid Shape Correspondence
Technical University of Munich · Simon Fraser University · Munich Center for Machine Learning +3
Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities
Apr 1, 2026Aleksei Zhuravlev, Lennart Bastian, Dongliang Cao +12Functional Maps Non-Rigid Shape Correspondence
MPI for Informatics · Imperial College London · Technical University of Munich +5
RINO: Rotation-Invariant Non-Rigid Correspondences
Mar 29, 2026Maolin Gao, Shao Jie Hu-Chen, Congyue Deng +3Functional Maps Non-Rigid Shape Correspondence
TUM · MCML · Stanford University +1
Lookalike3D: Seeing Double in 3D
Mar 25, 2026Chandan Yeshwanth, Angela Dai3D Shape Matching 3D Scene Understanding
Technical University of Munich
SGMatch: Semantic-Guided Non-Rigid Shape Matching with Flow Regularization
Mar 13, 2026Tianwei Ye, Xiaoguang Mei, Yifan Xia +3Functional Maps Geometric Representation Learning
Wuhan University, Wuhan 430072, China
CMHANet: A Cross-Modal Hybrid Attention Network for Point Cloud Registration
Mar 13, 2026Dongxu Zhang, Yingsen Wang, Yiding Sun +33D Shape Matching Point Cloud Registration
School of Software, Xi’an Jiaotong University, No. 28 Xianning West Road, China · CEPRI, China Electric Power Research Institute No. 15 Xiaoying East Road, Qinghe, Haidian District, Beijing, China
GLASS: Graph and Vision-Language Assisted Semantic Shape Correspondence
Mar 8, 2026Qinfeng Xiao, Guofeng Mei, Qilong Liu +53D Shape Matching Semantic Correspondence
Hong Kong Polytechnic University, HK SAR · Fondazione Bruno Kessler, Italy · Laboratory for Artificial Intelligence in Design, HK SAR +1
Universal 3D Shape Matching via Coarse-to-Fine Language Guidance
Feb 22, 2026Qinfeng Xiao, Guofeng Mei, Bo Yang +33D Shape Matching Semantic Correspondence
Hong Kong Polytechnic University, HK SAR · Fondazione Bruno Kessler, Italy · University of Technology Sydney, Australia
Affordance Transfer Across Object Instances via Semantically Anchored Functional Map
Feb 16, 2026Xiaoxiang Dong, Weiming ZhiFunctional Maps 3D Shape Matching
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA. · School of Computer Science, The University of Sydney, Australia. · Australian Centre for Robotics, The University of Sydney, Australia. +1
FUSE: A Flow-based Mapping Between Shapes
Nov 17, 2025Lorenzo Olearo, Giulio Viganò, Daniele Baieri +2Flow Matching Non-Rigid Shape Correspondence
University of Milano-Bicocca, Milan, Italy · University of Bonn, Bonn, Germany · Lamarr Institute, Germany +1

Nov 2, 2026J/K move · Enter open · S save

Blueprint2CAD: Benchmarking Foundation Models on Engineering Drawing-to-CAD Render Correspondence

Alexander Valverde, Danny Xie-Li, Zhichao Wang, Marcos V. Conde

Novineer · Instituto Tecnológico de Costa Rica · University of Würzburg

VLM Evaluation Cross-View Image Matching 3D Shape Matching

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Engineering drawings specify mechanical parts through orthographic projections. Recognizing which part a drawing depicts underlies part search, design reuse, and digitization, yet no public benchmark measures whether vision models can do it. We introduce Blueprint2CAD, a benchmark for drawing-to-render correspondence: given an orthographic line drawing and a shaded render, usually from another viewpoint, decide whether they show the same part. Its data layer, BlueCAD-30K, applies an open drafting pipeline to 29,925 Zero-to-CAD models, adding view-aligned renders, clean and dimensioned drawings from six viewpoints, and surface-type metadata. Frozen encoders retrieve the matching part from a 10,000-model gallery with up to 65% Recall@1 when viewpoints are known but 43% when they are not, and the four encoders we test lie within six points of each other: viewpoint, not backbone, is the bottleneck. Six zero-shot vision-language models reach only 0.52-0.67 verification accuracy, with model-specific Yes/No biases that chain-of-thought shifts but does not remove; a quick LoRA fine-tuning on in-domain pairs improves verification accuracy by a few points but sharpens these biases and leaves the viewpoint failure intact.