# DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

arXiv: https://arxiv.org/abs/2412.05268 · HTML: https://mlsanity.com/paper/2412.05268/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2024.

## Built on by (most cited first, 30 of 34)

- [ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models](https://mlsanity.com/paper/2506.16211/) (2025)
- [Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations](https://mlsanity.com/paper/2507.00990/) (2025)
- [Hierarchical Neural Semantic Representation for 3D Semantic Correspondence](https://mlsanity.com/paper/2509.17431/) (2025)
- [Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation](https://mlsanity.com/paper/2504.06961/) (2025)
- [MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence](https://mlsanity.com/paper/2508.13534/) (2025)
- [MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From Egocentric Videos](https://mlsanity.com/paper/2504.06084/) (2025)
- [MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Model for Embodied Task Planning](https://mlsanity.com/paper/2512.16909/) (2025)
- [Textured 3D Regenerative Morphing with 3D Diffusion Prior](https://mlsanity.com/paper/2502.14316/) (2025)
- [Action-Geometry Prediction with 3D Geometric Prior for Bimanual Manipulation](https://mlsanity.com/paper/2602.23814/) (2026)
- [Learning 3D Scene Analogies with Neural Contextual Scene Maps](https://mlsanity.com/paper/2503.15897/) (2025)
- [MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing](https://mlsanity.com/paper/2601.00204/) (2026)
- [Symmetry Understanding of 3D Shapes via Chirality Disentanglement](https://mlsanity.com/paper/2508.05505/) (2025)
- [Symmetry Informative and Agnostic Feature Disentanglement for 3D Shapes](https://mlsanity.com/paper/2601.14804/) (2026)
- [Tether: Autonomous Functional Play with Correspondence-Driven Trajectory Warping](https://mlsanity.com/paper/2603.03278/) (2026)
- [AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence](https://mlsanity.com/paper/2604.10579/) (2026)
- [RAPID: Robot Agentic Programming from Demonstrations](https://mlsanity.com/paper/2609.30249/) (2026)
- [IGen: Scalable Data Generation for Robot Learning from Open-World Images](https://mlsanity.com/paper/2512.01773/) (2025)
- [Lemon: A Unified and Scalable 3D Multimodal Model for Universal Spatial Understanding](https://mlsanity.com/paper/2512.12822/) (2025)
- [Generate, Transfer, Adapt: Learning Functional Dexterous Grasping from a Single Human Demonstration](https://mlsanity.com/paper/2601.05243/) (2026)
- [Universal 3D Shape Matching via Coarse-to-Fine Language Guidance](https://mlsanity.com/paper/2602.19112/) (2026)
- [GLASS: Graph and Vision-Language Assisted Semantic Shape Correspondence](https://mlsanity.com/paper/2603.07652/) (2026)
- [SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals](https://mlsanity.com/paper/2605.18039/) (2026)
- [Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence](https://mlsanity.com/paper/2605.30093/) (2026)
- [Quantum-Inspired Harmonic Decision Models: A Computational Framework for Music Generation](https://mlsanity.com/paper/2607.05007/) (2026)
- [Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks](https://mlsanity.com/paper/2511.02830/) (2025)
- [Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via Assistant Experts](https://mlsanity.com/paper/2603.12769/) (2026)
- [AffordMatcher: Affordance Learning in 3D Scenes from Visual Signifiers](https://mlsanity.com/paper/2603.27970/) (2026)
- [RAAP: Retrieval-Augmented Affordance Prediction with Cross-Image Action Alignment](https://mlsanity.com/paper/2603.29419/) (2026)
- [HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions](https://mlsanity.com/paper/2605.10201/) (2026)
- [Category-Level 3D Correspondence in Camera Space via Morphable Object Priors](https://mlsanity.com/paper/2605.28257/) (2026)

## Abstract (arXiv)

Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Compared to shape correspondence, semantic correspondence is more effective in generalizing across different object categories. To this end, we present DenseMatcher, a method capable of computing 3D correspondences between in-the-wild objects that share similar structures. DenseMatcher first computes vertex features by projecting multiview 2D features onto meshes and refining them with a 3D network, and subsequently finds dense correspondences with the obtained features using functional map. In addition, we craft the first 3D matching dataset that contains colored object meshes across diverse categories. In our experiments, we show that DenseMatcher significantly outperforms prior 3D matching baselines by 43.5%. We demonstrate the downstream effectiveness of DenseMatcher in (i) robotic manipulation, where it achieves cross-instance and cross-category generalization on long-horizon complex manipulation tasks from observing only one demo; (ii) zero-shot color mapping between digital assets, where appearance can be transferred between different objects with relatable geometry.