| --- |
| license: apache-2.0 |
| pipeline_tag: image-feature-extraction |
| --- |
| |
| # DeFM: Learning Foundation Representations from Depth for Robotics |
|
|
| <div align="center"> |
|
|
| [](https://opensource.org/licenses/Apache-2.0) |
| [](https://github.com/leggedrobotics/defm) |
| [](https://arxiv.org/abs/2601.18923) |
| [](https://de-fm.github.io/) |
| </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 |
|
|
| ### 1. Loading the Model |
| Load via **TorchHub** for easy integration: |
|
|
| ```python |
| import torch |
| |
| # Load the 307M Parameter Foundation Model |
| model = torch.hub.load('leggedrobotics/defm:main', 'defm_vit_l14', pretrained=True) |
| model.eval().to("cuda") |
| ``` |
|
|
| ### 2. Preprocessing |
| DeFM requires depth maps to be processed into our metric-aware 3-channel format. |
|
|
| ```python |
| from defm import preprocess_depth_image |
| |
| # Depth needs to be in meters (numpy array, tensor or PIL image) |
| normalized_depth = preprocess_depth_image(metric_depth, target_size=518, patch_size=14) |
| ``` |
|
|
| ### 3. Inference |
| ```python |
| with torch.no_grad(): |
| output = model.get_intermediate_layers( |
| normalized_depth, n=1, reshape=True, return_class_token=True) |
| |
| spatial_tokens = output[0][0] # (B, C, H', W') |
| class_token = output[0][1] # (B, C) |
| ``` |
|
|
| For more details, visit our [GitHub repository](https://github.com/leggedrobotics/defm). |
|
|
| ## 📊 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}, |
| } |
| ``` |
