---
title: 3d-jepa
canonical_url: "https://www.modelscope.cn/models/facebook/3d-jepa"
md_url: "https://www.modelscope.cn/models/facebook/3d-jepa.md"
repository: facebook/3d-jepa
last_updated: 2025-05-20
license: cc-by-nc-4.0
parameters: 242.2M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 335
stars: 0
---

# 3d-jepa

> 3d-jepa - facebook 在 ModelScope 开源的模型。Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D

facebook/3d-jepa 是 ModelScope 魔搭社区上的 242.2M 参数机器学习模型，采用 cc-by-nc-4.0 许可。

- **Repository**: facebook/3d-jepa
- **License**: cc-by-nc-4.0
- **Parameters**: 242.2M
- **Downloads**: 335
- **Stars**: 0
- **Last updated**: 2025-05-20

Source: https://www.modelscope.cn/models/facebook/3d-jepa

---

# Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D

Official model weights for the `Locate-3D` models and the `3D-JEPA` encoders

## Locate 3D

Locate 3D is a model for localizing objects in 3D scenes from referring expressions like “the
small coffee table between the sofa and the lamp.” Locate 3D sets a new state-of-the-art on standard
referential grounding benchmarks and showcases robust generalization capabilities. Notably, Locate
3D operates directly on sensor observation streams (posed RGB-D frames), enabling real-world
deployment on robots and AR devices. 

## 3D-JEPA

3D-JEPA, a novel self-supervised
learning (SSL) algorithm applicable to sensor point clouds, is key to `Locate 3D`. It takes as input a 3D pointcloud
featurized using 2D foundation models (CLIP, DINO). Subsequently, masked prediction in latent space
is employed as a pretext task to aid the self-supervised learning of contextualized pointcloud features.
Once trained, the 3D-JEPA encoder is finetuned alongside a language-conditioned decoder to jointly
predict 3D masks and bounding boxes. 

## Models

- **Locate-3D**: Locate-3D model trained on public referential grounding datasets
- **Locate-3D+**: Locate-3D model trained on public referential grounding datasets and the newly released Locate 3D Dataset
- **3D-JEPA**: Pre-trained SSL encoder for 3D understanding


## How to Use

For detailed instructions on how to load the encoder and integrate it into your downstream task, please refer to our [GitHub repository](https://github.com/facebookresearch/locate-3d).

## License

The majority of `locate-3` is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Pointcept is licensed under the MIT license.