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gaussiancar

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🚕 [IEEE ICRA 2026] GaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion

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# <div align="center">**🚀 GaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion**</div>

<a href="https://www.santimontiel.eu/">Santiago Montiel-Marín</a>1,
 <a href="https://www.miguelantunes.eu/">Miguel Antunes-García</a>1,
 <a href="https://www.linkedin.com/in/fabio-sanchez-garcia/">Fabio Sánchez-García</a>1,


 <a href="https://allamazares.jimdofree.com/">Ángel Llamazares</a>1,
 <a href="https://sites.google.com/it-caesar.de/homepage/">Holger Caesar</a>2, and
 <a href="http://www.robesafe.uah.es/personal/bergasa/">Luis M. Bergasa</a>1

1 Universidad de Alcalá,
 2 Technical University of Delft

📝 <a href="https://arxiv.org/abs/2602.08784">Paper</a> ·
 💻 <a href="https://www.santimontiel.eu/projects/gaussiancar">Project Page</a> ·
 🤗 <a href="https://huggingface.co/santimontieleu/gaussiancar">Weights</a>

Accepted to IEEE ICRA 2026! ✨

## 📰 News

**[11 Feb. 2026]**: We release code and weights!\
**[31 Jan. 2026]**: GaussianCaR has been accepted for publication at IEEE ICRA 2026! See you in Vienna, Austria!

## 🔍 Abstract

Robust and accurate perception of dynamic objects and map elements is crucial for autonomous vehicles performing safe navigation in complex traffic scenarios. While vision-only methods have become the de facto standard due to their technical advances, they can benefit from effective and **cost-efficient fusion with radar measurements**. In this work, we advance fusion methods by **repurposing Gaussian Splatting** as an *efficient universal view transformer* that bridges the view disparity gap, mapping both image pixels and radar points into a common Bird’s-Eye View (BEV) representation.

Our main contribution is **GaussianCaR**, an end-to-end network for BEV segmentation that, unlike prior BEV fusion methods, leverages Gaussian Splatting to map raw sensor information into latent features for efficient camera-radar fusion. Our architecture combines multi-scale fusion with a transformer decoder to efficiently extract BEV features.

Experimental results demonstrate that our approach achieves **performance** on par with, or even surpassing, the state-of-the-art on BEV segmentation tasks (57.3%, 82.9%, 50.1% IoU for vehicles, roads, and lane dividers) on the nuScenes dataset, while maintaining a ***3.2x faster inference runtime***.

**⭐ Key contributions:**
* **Gaussian Splatting** as a *universal view transformer* for heterogeneous camera–radar fusion in BEV.
* End-to-end *differentiable* **pixels/points → Gaussians → BEV** pipeline without voxelization or depth discretization.
* **State-of-the-art BEV segmentation** efficiency, achieving comparable or better accuracy than prior fusion methods with ***3.2x faster inference***.

## 🚀 Getting Started

### 1. Download the nuScenes Dataset

Download the nuScenes dataset from the official website: **trainval**, **mini**, and **map expansion**.
Extract it so that the directory structure looks like this:

```shell
 <your/path/to/nuscenes>/
 ├──── maps/
 ├──── samples/
 ├──── sweeps/
 ├──── v1.0-trainval/
 └──── v1.0-mini/
```

> [!NOTE]
> The nuScenes dataset is large (~400GB). Make sure you have enough disk space before downloading.

Then, set the `PATH_TO_NUSCENES` environment variable to point to the dataset root:

```shell
export PATH_TO_NUSCENES=<your/path/to/nuscenes>
```

This variable will later be consumed by the `Dockerfile`, for example:
```shell
PATH_TO_NUSCENES := $(shell echo $$PATH_TO_NUSCENES)
```

You can also automate logging with **Wandb** by setting the following environment variable:

```shell
export WANDB_API_KEY=<your_wandb_api_key>
```

### 2. Build and run your container
We provide a `Dockerfile` and a `Makefile` to facilitate the setup of the required environment. To build the Docker image, run the following command in the root directory of the repository:

```shell
make build
```

To run the Docker container and build the environment, use the following command:

```shell
make run
```

If the previous command is successful, you should now be inside the Docker container and
see a prompt like this:

```shell
------------------------------------ System info -----------------------------------

🔄 Checking virtual environment...
✅ .venv found

🔍 Checking datasets availability...
✅ nuScenes dataset at /data/nuscenes found!

🔍 Checking GPU and CUDA availability...
✅ PyTorch is working properly with the GPU.
📍 GPU Information:
   - CUDA version:     12.9
   - Device name:      NVIDIA GeForce RTX 5090
   - Number of GPUs:   1

------------------------------------------------------------------------------------
<your-user>@<your-device>:/workspace →
```

### 3. Preprocess the nuScenes Dataset

We use `uv` to manage our experiments inside the Docker container and `hydra` for configuration management.
Once inside the Docker container, preprocess the nuScenes dataset by running:

```shell
uv run tools/create_data.py
```

This will create the necessary data files for training and evaluation.

```shell
 <your/path/to/nuscenes>/
 ├──── maps/
 ├──── samples/
 ├──── sweeps/
 ├──── v1.0-trainval/
 └──── v1.0-mini/
 └──── labels/ # NEW!!!
 └──── scene-0001/
 └──── scene-0001.json
 └──── ...
 └──── ...
```

### 4. Run the training script!
You can now train the **GaussianCaR** model for either the vehicle segmentation or map segmentation tasks. Please, set the `debug` flag to `False` in the configuration files located in the `configs/` directory for full training runs.

```shell
uv run tools/train.py task=vehicle
```

And, for the map segmentation task:

```shell
uv run tools/train.py task=map
```

The training runs are highly customizable via the configuration files located in the `configs/` directory. You can modify hyperparameters, model architecture, and other settings by editing these files or by passing command-line arguments.

An example of how to change the batch size and number of devices available directly from the command line is shown below:

```shell
uv run tools/train.py task=vehicle trainer.batch_size=8 optimizer.lr=0.0001
```

> [!WARNING]
> At this stage, training is limited to full precision (FP32).
> Mixed-precision (FP16/BF16) is currently unsupported due to the lack of
> backward support in the **Points-to-Gaussians** module (PTv3-based).

### 5. Validate your run

Once training is complete, you can validate the trained model using the following command:

```shell
uv run tools/eval.py task=vehicle checkpoint_path=<path_to_your_checkpoint>
```

## 🏋️‍♂️ Pretrained Weights

You can find the pretrained weights for **GaussianCaR** on our [Hugging Face repository](https://huggingface.co/santimontieleu/gaussiancar).

| Task                  | Dataset  | Pretrained Weights Link             |
|-----------------------|----------|-------------------------------------|
| Vehicle Segmentation  | nuScenes | [Download](https://huggingface.co/santimontieleu/gaussiancar/blob/main/nuscenes_vehicle.ckpt) |
| Map Segmentation      | nuScenes | [Download](https://huggingface.co/santimontieleu/gaussiancar/blob/main/nuscenes_map.ckpt)     |

## 📈 Experimental Results

*Table I. BEV Vehicle Segmentation on the nuScenes Validation Set.*

| Type | Method | Cam Enc | Radar Enc | IoU (↑) |
|------------------|------------------------|-----------|-----------|------------------|
| **Camera-only** | BEVFormer | RN-101 | - | 43.2 |
| | GaussianLSS | RN-101 | - | 46.1 |
| | SimpleBEV | RN-101 | - | 47.4 |
| | PointBeV | EN-64 | - | 47.8 |
| | GaussianBeV | EN-64 | - | 50.3 |
| **Camera-radar** | SimpleBEV++ | RN-101 | PFE+Conv | 52.7 |
| | SimpleBEV | RN-101 | Conv | 55.7 |
| | BEVCar | DINOv2/B | PFE+Conv | 58.4 |
| | CRN | RN-50 | SECOND | <ins>58.8</ins> |
| | BEVGuide | EN-64 | SECOND | **59.2** |
| | **GaussianCaR (ours)** | EVIT-L2 | PTv3 | 57.3 |

*Table II. BEV Map Segmentation on the nuScenes Validation Set.*

| Type | Method | Driv. Area IoU (↑) | Lane Div. IoU (↑) |
|------------------|------------------------|---------------------|--------------------|
| **Camera-only** | LSS | 72.9 | 20.0 |
| | BEVFormer | 80.1 | 25.7 |
| | GaussianBeV | 82.6 | <ins>47.4</ins> |
| **Camera-radar** | BEVGuide | 76.7 | 44.2 |
| | Simple-BEV++ | 81.2 | 40.4 |
| | BEVCar | **83.3** | 45.3 |
| | **GaussianCaR (ours)** | <ins>82.9</ins> | **50.1** |

*Table III. Inference Speed Comparison on an NVIDIA RTX 4090.*

| Method | Veh. IoU (↑) | ms (↓) | FPS (↑) |
|---------------------------|-----------------|-----------------|-----------------|
| Simple-BEV | 55.7 | **57.6** | **17.4** |
| Simple-BEV++ | 52.7 | 211.3 | 4.7 |
| BEVCar | **58.4** | 245.6 | 4.1 |
| **GaussianCaR (vehicle)** | <ins>57.3</ins> | <ins>75.6</ins> | <ins>13.2</ins> |
| **GaussianCaR (map)** | - | 81.1 | 12.3 |

## 🖊️ Citation
If you find this work useful for your research, please consider citing us following the BibTeX entry:

```bibtex
@article{montielmarin2026gaussiancar,
  title         = {GaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion},
  author        = {Montiel-Marín, Santiago and Antunes-García, Miguel and
                  Sánchez-García, Fabio and Llamazares, Ángel and
                  Caesar, Holger and Bergasa, Luis M.},
  year          = {2026},
  eprint        = {2602.08784},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO}
}
```

>[!NOTE]
> We will replace the current arXiv citation with the official ICRA 2026 proceedings once available.

## 📮 Contact

If you have any questions or suggestions, feel free to open an issue or contact us directly via email at: santiago.montiel@uah.es.

## 📄 License

This project is released under the Apache-2.0 License.
See [LICENSE](LICENSE) for details.

## 🫂 Acknowledgements

This work has been supported by projects PID2021-126623OB-I00 and PID2024-161576OB-I00, funded by MCIN/AEI/10.13039/501100011033 and co-funded by the European Regional Development Fund (ERDF, “A way of making Europe”), by project PLEC2023-010343 (INARTRANS 4.0) funded by MCIN/AEI/10.13039/501100011033, and by the R&D program TEC-2024/TEC-62 (iRoboCity2030-CM) and ELLIS Unit Madrid, granted by the Community of Madrid.

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