Create README.md

README.md

@@ -1,5 +1,110 @@
 ---
 license: cc-by-nc-4.0
+language:
+  - en
+tags:
+  - radar
+  - lidar
+  - diffusion
+  - robotics
+  - point-cloud
+  - image-to-image
 base_model:
-- prs-eth/marigold-depth-v1-1
----
+  - prs-eth/marigold-depth-v1-1
+---
+
+# RadarSFD
+
+RadarSFD reconstructs dense LiDAR-like point cloud representations from a single radar frame using conditional latent diffusion with pretrained priors. This repository contains the released RadarSFD checkpoint for the paper *RadarSFD: Single-Frame Diffusion with Pretrained Priors for Radar Point Clouds*.
+
+The released weights are intended to be used together with the official project code:
+
+- GitHub: https://github.com/phi-lab-rice/RadarSFD
+
+## Model description
+
+RadarSFD conditions a latent diffusion denoiser on a single radar range-azimuth BEV image and predicts a dense LiDAR-like BEV reconstruction. The model transfers geometric priors from a pretrained Marigold U-Net and uses latent-space conditioning from the radar input during denoising.
+
+In the current release, the uploaded checkpoint corresponds to the trained U-Net weights used by the project codebase.
+
+## Intended use
+
+This model is intended for:
+
+- research on radar-based 3D perception
+- single-frame radar-to-LiDAR-like reconstruction
+- benchmarking on the RadarHD dataset
+- qualitative and quantitative evaluation using the official repository
+
+This model is not intended as a plug-and-play general-purpose Hugging Face inference API model. It is designed to be loaded by the RadarSFD codebase for evaluation and research experiments.
+
+## Input and output
+
+Input:
+
+- a single radar BEV image from the RadarHD-style preprocessing pipeline
+- grayscale radar input repeated to 3 channels by the project dataloader
+
+Output:
+
+- a reconstructed LiDAR-like BEV image
+- downstream evaluation artifacts produced by the repository evaluation scripts
+
+Within the official codebase, radar images are loaded as grayscale images and repeated to 3 channels. The training pipeline pairs radar and LiDAR BEV images from the RadarHD dataset.
+
+## How to use
+
+Clone the official repository and follow its environment setup and evaluation instructions:
+
+```bash
+git clone https://github.com/phi-lab-rice/RadarSFD.git
+cd RadarSFD
+```
+
+Place the checkpoint in your desired location, update the dataset path in `Code/config.yaml`, and run evaluation with:
+
+```bash
+python3 Code/Eval/eval.py --config Code/config.yaml --checkpoint /path/to/RadarSFD.safetensors
+```
+
+Please refer to the repository for the expected dataset structure, environment dependencies, and evaluation workflow.
+
+## Training data
+
+RadarSFD is trained and evaluated on the RadarHD dataset:
+
+- https://github.com/akarsh-prabhakara/RadarHD
+
+The project uses paired radar and LiDAR BEV images for training, validation, and testing.
+
+## Architecture and training
+
+- Backbone initialization: `prs-eth/marigold-depth-v1-1`
+- VAE mode in the released code: TAESD (`madebyollin/taesd`)
+- Diffusion scheduler for training: DDPM
+- Inference scheduler in evaluation: DDIM
+- Training objective: latent diffusion with additional perceptual losses in image space
+
+## Limitations
+
+- This release is research-oriented and has only been validated within the official RadarSFD codebase.
+- Performance depends on using the same preprocessing and dataset conventions as the training setup.
+- The model is designed for single-frame radar reconstruction and does not use temporal accumulation or SAR.
+- Outputs are LiDAR-like BEV reconstructions rather than fully post-processed 3D point clouds ready for deployment.
+
+## License
+
+This model is released under the `CC-BY-NC-4.0` license. Please review the dataset and upstream model licenses as well before use.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@inproceedings{zhao2026radarsfd,
+  title     = {RadarSFD: Single-Frame Diffusion with Pretrained Priors for Radar Point Clouds},
+  author    = {Zhao, Bin and Garg, Nakul},
+  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
+  year      = {2026}
+}
+```