---
pipeline_tag: graph-ml
library_name: pytorch
license: apache-2.0
tags:
- 3d
- point-cloud
- self-supervised-learning
---

# Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations

This repository contains the model weights for **Concerto**, a novel approach for learning robust spatial representations presented in the paper [Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations](https://huggingface.co/papers/2510.23607).

- **Paper:** [Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations](https://huggingface.co/papers/2510.23607)
- **Project Page:** [https://pointcept.github.io/Concerto/](https://pointcept.github.io/Concerto)
- **Codebase:** [https://github.com/Pointcept/Pointcept](https://github.com/Pointcept/Pointcept)
- **Inference:** [https://github.com/Pointcept/Concerto](https://github.com/Pointcept/Concerto)

## Models
The default models(concerto_large/base/small/tiny) are the pre-release version of our next work, which can deal with input without color and normal. We pre-release these for general public use because many tasks lack such information. The original Concerto model is `concerto_base_origin.pth`.

## Abstract
Humans learn abstract concepts through multisensory synergy, and once formed, such representations can often be recalled from a single modality. Inspired by this principle, we introduce Concerto, a minimalist simulation of human concept learning for spatial cognition, combining 3D intra-modal self-distillation with 2D-3D cross-modal joint embedding. Despite its simplicity, Concerto learns more coherent and informative spatial features, as demonstrated by zero-shot visualizations. It outperforms both standalone SOTA 2D and 3D self-supervised models by 14.2% and 4.8%, respectively, as well as their feature concatenation, in linear probing for 3D scene perception. With full fine-tuning, Concerto sets new SOTA results across multiple scene understanding benchmarks (e.g., 80.7% mIoU on ScanNet). We further present a variant of Concerto tailored for video-lifted point cloud spatial understanding, and a translator that linearly projects Concerto representations into CLIP's language space, enabling open-world perception. These results highlight that Concerto emerges spatial representations with superior fine-grained geometric and semantic consistency.

## Usage
For detailed installation, data preparation, training, and testing instructions, please refer to the [official codebase](https://github.com/Pointcept/Pointcept) and [inference demo](https://github.com/Pointcept/Concerto).

## Citation
If you find Concerto or the Pointcept codebase useful in your research, please cite the following papers:

```bibtex
@misc{pointcept2023,
    title={Pointcept: A Codebase for Point Cloud Perception Research},
    author={Pointcept Contributors},
    howpublished = {\url{https://github.com/Pointcept/Pointcept}},
    year={2023}
}

@article{zhang2025concerto,
  title={Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations},
  author={Zhang, Yujia and Wu, Xiaoyang and Lao, Yixing and Wang, Chengyao and Tian, Zhuotao and Wang, Naiyan and Zhao, Hengshuang},
  journal={Conference on Neural Information Processing Systems},
  year={2025},
}
```