---
license: apache-2.0
pipeline_tag: image-feature-extraction
---
# DeFM: Learning Foundation Representations from Depth for Robotics

<div align="center">

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-green.svg?style=for-the-badge)](https://opensource.org/licenses/Apache-2.0)
[![GitHub](https://img.shields.io/badge/GitHub-Code-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/leggedrobotics/defm)
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</div>

---

**DeFM** (Depth Foundation Model) is a vision backbone trained on **60M depth images** via self-distillation. It is engineered for robotic perception, providing metric-aware representations that excel in sim-to-real transfer and cross-sensor generalization.

TL;DR - A DINO-style encoder, but for depth image inputs.

## 🌟 Key Features
- **Large-Scale Pretraining**: We pretrain on our curated dataset of 60 M depth images using self-distillation.
- **Semantic Awareness**: DeFM learns not only robust geometric priors but also semantically rich features from just depth images.
- **Metric-Aware Normalization**: Our novel three channel input normalization preserves metric depth across multiple scales.
- **Compact efficient models**: We distill our DeFM-ViT-L into a family of smaller efficient CNNs as small as 3M params for robot policy learning.
- **Robotics Proven**: Our encoder is proven effective for diverse robotic tasks such as navigation, manipulation and locomotion without task-specific fine-tuning.

## Usage

Visit our [github repo](https://github.com/leggedrobotics/defm) for details on how to use the models.

## 📊 Model Zoo

The following table provides a comprehensive overview of the DeFM model family, including architectural parameters, inference latency across training and deployment hardware (224x224), and performance on the ImageNet-1k-Depth benchmark.

| Model | Params (M) | RTX 4090 (ms) | Jetson Orin (ms) | Top-5 KNN (%) | Linear Prob (%) | Checkpoint |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| **DeFM ViT-L/14** | 307.0 | 624.91 | 72.82 | 84.79 | 71.72 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_vit_l14.pth) |
| **DeFM ViT-S/14** | 22.1 | 63.76 | 11.92 | 78.06 | 61.54 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_vit_s14.pth) |
| **DeFM ResNet-50** | 26.2 | 69.39 | 17.79 | 77.63 | 61.54 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_resnet50.pth) |
| **DeFM ResNet-34** | 21.8 | 33.08 | 13.54 | 72.72 | 54.39 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_resnet34.pth) |
| **DeFM ResNet-18** | 11.7 | 21.06 | 8.67 | 69.69 | 50.58 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_resnet18.pth) |
| **DeFM EfficientNet-B6** | 28.98 | 150.98 | 54.11 | 77.81 | 59.23 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_efficientnet_b6.pth) |
| **DeFM EfficientNet-B4** | 14.16 | 86.51 | 39.67 | 74.74 | 54.73 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_efficientnet_b4.pth) |
| **DeFM EfficientNet-B2** | 4.95 | 46.12 | 28.37 | 71.51 | 50.32 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_efficientnet_b2.pth) |
| **DeFM EfficientNet-B0** | 3.01 | 29.39 | 21.04 | 67.98 | 46.17 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_efficientnet_b0.pth) |
| **DeFM RegNetY-1.6GF** | 12.4 | 44.25 | 41.82 | 76.21 | 57.28 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_regnet_y_1_6gf.pth) |
| **DeFM RegNetY-800MF** | 6.3 | 25.21 | 24.16 | 74.91 | 57.03 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_regnet_y_800mf.pth) |
| **DeFM RegNetY-400MF** | 4.1 | 17.27 | 25.17 | 72.87 | 50.51 | [Download](https://huggingface.co/leggedrobotics/defm/resolve/main/defm_regnet_y_400mf.pth) |
---

## 📖 Citation
If you find DeFM useful for your research, please cite our paper:

```
@misc{patel2026defm,
      title={DeFM: Learning Foundation Representations from Depth for Robotics}, 
      author={Manthan Patel and Jonas Frey and Mayank Mittal and Fan Yang and Alexander Hansson and Amir Bar and Cesar Cadena and Marco Hutter},
      year={2026},
      eprint={2601.18923},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2601.18923}, 
}
```