# MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis

arXiv: https://arxiv.org/abs/2207.10228 · HTML: https://mlsanity.com/paper/2207.10228/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 69 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 30 of 48)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling](https://mlsanity.com/paper/2111.14819/) (2021)
- [End-to-End Human Pose and Mesh Reconstruction with Transformers](https://mlsanity.com/paper/2012.09760/) (2020)
- [MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers](https://mlsanity.com/paper/2012.00759/) (2020)
- [An End-to-End Transformer Model for 3D Object Detection](https://mlsanity.com/paper/2109.08141/) (2021)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [PolyGen: An Autoregressive Generative Model of 3D Meshes](https://mlsanity.com/paper/2002.10880/) (2020)
- [Mesh Graphormer](https://mlsanity.com/paper/2104.00272/) (2021)
- [ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias](https://mlsanity.com/paper/2106.03348/) (2021)
- [Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2011.02910/) (2020)
- [MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2111.12707/) (2021)
- [Learning Features by Watching Objects Move](https://mlsanity.com/paper/1612.06370/) (2016)

## Built on by (most cited first, 30 of 31)

- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining](https://mlsanity.com/paper/2503.18052/) (2025)
- [Masked Image Modeling: A Survey](https://mlsanity.com/paper/2408.06687/) (2024)
- [ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining](https://mlsanity.com/paper/2408.10906/) (2024)
- [NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields](https://mlsanity.com/paper/2404.01300/) (2024)
- [3D Mesh Editing using Masked LRMs](https://mlsanity.com/paper/2412.08641/) (2024)
- [Masked Modeling for Self-supervised Representation Learning on Vision and Beyond](https://mlsanity.com/paper/2401.00897/) (2023)
- [Scene123: One Prompt to 3D Scene Generation via Video-Assisted and Consistency-Enhanced MAE](https://mlsanity.com/paper/2408.05477/) (2024)
- [DreamCS: Geometry-Aware Text-to-3D Generation with Unpaired 3D Reward Supervision](https://mlsanity.com/paper/2506.09814/) (2025)
- [Describe, Adapt and Combine: Empowering CLIP Encoders for Open-set 3D Object Retrieval](https://mlsanity.com/paper/2507.21489/) (2025)
- [Token Boosting for Robust Self-Supervised Visual Transformer Pre-training](https://mlsanity.com/paper/2304.04175/) (2023)
- [Aerial Image Object Detection With Vision Transformer Detector (ViTDet)](https://mlsanity.com/paper/2301.12058/) (2023)
- [HodgeFormer: Transformers for Learnable Operators on Triangular Meshes through Data-Driven Hodge Matrices](https://mlsanity.com/paper/2509.01839/) (2025)
- [Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry](https://mlsanity.com/paper/2504.11418/) (2025)
- [TeDA: Boosting Vision-Lanuage Models for Zero-Shot 3D Object Retrieval via Testing-time Distribution Alignment](https://mlsanity.com/paper/2505.02325/) (2025)
- [SieveNet: Selecting Point-Based Features for Mesh Networks](https://mlsanity.com/paper/2308.12530/) (2023)
- [A Recipe for Geometry-Aware 3D Mesh Transformers](https://mlsanity.com/paper/2411.00164/) (2024)
- [Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining](https://mlsanity.com/paper/2501.09597/) (2025)
- [CHaRM: Conditioned Heatmap Regression Methodology for Accurate and Fast Dental Landmark Localization](https://mlsanity.com/paper/2501.13073/) (2025)
- [SSA3D: Text-Conditioned Assisted Self-Supervised Framework for Automatic Dental Abutment Design](https://mlsanity.com/paper/2512.11507/) (2025)
- [Instance-aware 3D Semantic Segmentation powered by Shape Generators and Classifiers](https://mlsanity.com/paper/2311.12291/) (2023)
- [Social-MAE: Social Masked Autoencoder for Multi-person Motion Representation Learning](https://mlsanity.com/paper/2404.05578/) (2024)
- [ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental Learning](https://mlsanity.com/paper/2409.12326/) (2024)
- [Text Condition Embedded Regression Network for Automated Dental Abutment Design](https://mlsanity.com/paper/2511.22578/) (2025)
- [Exploring 3D Dataset Pruning](https://mlsanity.com/paper/2603.00651/) (2026)
- [Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design](https://mlsanity.com/paper/2604.09047/) (2026)
- [P3T: Prototypical Point-level Prompt Tuning with Enhanced Generalization for 3D Vision-Language Models](https://mlsanity.com/paper/2604.15703/) (2026)
- [KDH-CAD: Knowledge-data hybrid CAD learning under data scarcity](https://mlsanity.com/paper/2606.01702/) (2026)
- [Intrinsic and Triangulation-Agnostic Attention: A Simple and Powerful Approach for Learning on Meshes](https://mlsanity.com/paper/2607.24954/) (2026)
- [GLOSS: Geometric Local Self-Similarity Learning for Faithful Reference-Guided Texture Fill](https://mlsanity.com/paper/2608.25461/) (2026)

## Abstract (arXiv)

Recently, self-supervised pre-training has advanced Vision Transformers on various tasks w.r.t. different data modalities, e.g., image and 3D point cloud data. In this paper, we explore this learning paradigm for 3D mesh data analysis based on Transformers. Since applying Transformer architectures to new modalities is usually non-trivial, we first adapt Vision Transformer to 3D mesh data processing, i.e., Mesh Transformer. In specific, we divide a mesh into several non-overlapping local patches with each containing the same number of faces and use the 3D position of each patch's center point to form positional embeddings. Inspired by MAE, we explore how pre-training on 3D mesh data with the Transformer-based structure benefits downstream 3D mesh analysis tasks. We first randomly mask some patches of the mesh and feed the corrupted mesh into Mesh Transformers. Then, through reconstructing the information of masked patches, the network is capable of learning discriminative representations for mesh data. Therefore, we name our method MeshMAE, which can yield state-of-the-art or comparable performance on mesh analysis tasks, i.e., classification and segmentation. In addition, we also conduct comprehensive ablation studies to show the effectiveness of key designs in our method.