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

Junzhe Zhu, Yuanchen Ju, Junyi Zhang, Muhan Wang, Zhecheng Yuan +2 more
2/12/2026

No confident code implementation found for this paper. 1 possible match can be shown below.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Deep Demonstration Tracing: Learning Generalizable Imitator Policy for Runtime Imitation from a Single Demonstration

93% match1 repo62026

3DFG-PIFu: 3D Feature Grids for Human Digitization from Sparse Views

93% match2026

MMA: Multi-Modal Adapter for Vision-Language Models

88% match1 repo1012026

Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning

88% match2026

Unmixing Before Fusion: A Generalized Paradigm for Multi-Source-Based Hyperspectral Image Synthesis

88% match2026

Cocktail: Mixing Multi-Modality Control for Text-Conditional Image Generation

88% match2026

Cite this paper

@article{zhu2026densematcher,
  title  = {DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo},
  author = {Junzhe Zhu and Yuanchen Ju and Junyi Zhang and Muhan Wang and Zhecheng Yuan and Kaizhe Hu and Huazhe Xu},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:0c79aa31e40f7795f2cbec699c623ee56fa0d6a3},
  journal = {ICLR 2025 2025}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/0c79aa31e40f7795f2cbec699c623ee56fa0d6a3.svg)](https://codeofpaper.com/paper/0c79aa31e40f7795f2cbec699c623ee56fa0d6a3)

Discussion