mlsanityData to 14 Oct 2026

MeshNet: Mesh Neural Network for 3D Shape Representation

arXiv 1811.11424 ↗First posted 28 Nov 2018cs.CVcited by 95

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Abstract (arXiv)

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.

Key facts. Cited by 95 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Nov 2018.

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Citing papers per year: 2021: 20; 2022: 13; 2023: 17; 2024: 18; 2025: 16; 2026: 11.

95 papers.

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  1. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 279
  2. 2022Towards Implicit Text-Guided 3D Shape Generationcited by 68
  3. 2023Deep Learning on Implicit Neural Representations of Shapescited by 63
  4. 2021Subdivision-Based Mesh Convolution Networkscited by 60
  5. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  6. 2024GeoGaussian: Geometry-aware Gaussian Splatting for Scene Renderingcited by 52
  7. 2023Geometric Clifford Algebra Networkscited by 38
  8. 2024GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priorscited by 38
  9. 2023High-Fidelity 3D Face Generation from Natural Language Descriptionscited by 33
  10. 2022MeshMAE: Masked Autoencoders for 3D Mesh Data Analysiscited by 31
  11. 20213D Pose Transfer with Correspondence Learning and Mesh Refinementcited by 30
  12. 2024Query2CAD: Generating CAD models using natural language queriescited by 29
38 more
  1. 20213D Adversarial Attacks Beyond Point Cloudcited by 26
  2. 2021Multimodal Semi-Supervised Learning for 3D Objectscited by 24
  3. 2021Voint Cloud: Multi-View Point Cloud Representation for 3D Understandingcited by 22
  4. 2022Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstructioncited by 21
  5. 2024Large Language Model Agent as a Mechanical Designercited by 21
  6. 2021NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Droppingcited by 16
  7. 2024Geometric Deep Learning for Computer-Aided Design: A Surveycited by 14
  8. 2024A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentationcited by 14
  9. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  10. 2022Laplacian2Mesh: Laplacian-Based Mesh Understandingcited by 11
  11. 20223DeformRS: Certifying Spatial Deformations on Point Cloudscited by 10
  12. 2021PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representationcited by 9
  13. 2022What's the Situation with Intelligent Mesh Generation: A Survey and Perspectivescited by 9
  14. 2024E(3)-Equivariant Mesh Neural Networkscited by 9
  15. 2025Bringing Attention to CAD: Boundary Representation Learning via Transformercited by 9
  16. 2025BRepFormer: Transformer-Based B-rep Geometric Feature Recognitioncited by 9
  17. 2021TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshescited by 8
  18. 2023SENS: Part-Aware Sketch-based Implicit Neural Shape Modelingcited by 8
  19. 2024DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstructioncited by 8
  20. 2021Intuitive Shape Editing in Latent Spacecited by 7
  21. 2022SphereDepth: Panorama Depth Estimation from Spherical Domaincited by 7
  22. 2022MVTN: Learning Multi-View Transformations for 3D Understandingcited by 7
  23. 2024Depth-Guided Robust and Fast Point Cloud Fusion NeRF for Sparse Input Viewscited by 6
  24. 2021AttWalk: Attentive Cross-Walks for Deep Mesh Analysiscited by 5
  25. 2024Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fieldscited by 5
  26. 2025Describe, Adapt and Combine: Empowering CLIP Encoders for Open-set 3D Object Retrievalcited by 5
  27. 2021IFCNet: A Benchmark Dataset for IFC Entity Classificationcited by 4
  28. 2021Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classificationcited by 4
  29. 2021LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognitioncited by 4
  30. 2021CloudWalker: Random walks for 3D point cloud shape analysiscited by 4
  31. 2022Representation-Agnostic Shape Fieldscited by 4
  32. 2022Be Careful with Rotation: A Uniform Backdoor Pattern for 3D Shapecited by 4
  33. 2021Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer's Diseasecited by 3
  34. 2022Random Walks for Adversarial Meshescited by 3
  35. 2023Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passingcited by 3
  36. 2024Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Predictioncited by 3
  37. 20253D Neural Operator-Based Flow Surrogates around 3D geometries: Signed Distance Functions and Derivative Constraintscited by 3
  38. 2023MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Cloudscited by 2

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