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README.md

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+---
+license: cc-by-nc-4.0
+tags:
+  - 3d-scene-generation
+  - latent-diffusion
+  - autonomous-driving
+  - kitti-360
+  - primitives
+  - cvpr-2026
+library_name: pytorch
+---
+
+# PrITTI: Primitive-based Generation of Controllable and Editable 3D Semantic Urban Scenes
+
+<p align="center">
+  <a href="https://drive.google.com/file/d/1Q7KgGbynzcIEyFJV1I17HgrYz6xrOwRJ/view?usp=sharing">📄 Paper</a> &nbsp;|&nbsp;
+  <a href="https://raniatze.github.io/pritti/">🌐 Project Page</a> &nbsp;|&nbsp;
+  <a href="https://github.com/autonomousvision/pritti">💻 Code</a>
+</p>
+
+<p align="center">
+  <img src="teaser.png" alt="PrITTI teaser" width="95%">
+</p>
+
+This repository hosts the **pre-trained checkpoints** for **PrITTI** (CVPR 2026), a latent-diffusion framework for controllable and editable 3D semantic urban scene generation, trained on [KITTI-360](https://www.cvlibs.net/datasets/kitti-360/).
+
+## Released Checkpoints
+
+| File | Size | Description |
+|------|------|-------------|
+| `lvae.ckpt` | 1.1 GB | Layout Variational Autoencoder, trained for 300 epochs (`epoch=299, step=580200`). |
+| `ldm_b/` | 773 MB | DiT-B Latent Diffusion Model in `diffusers`-pipeline format (`model_index.json` + `transformer/` + `decoder/` + `scheduler/`). |
+
+## Quick Start
+
+Full environment setup, preprocessing, training, inference, and evaluation instructions live in the [official GitHub repository](https://github.com/autonomousvision/pritti). The snippet below downloads both checkpoints into the locations the code expects:
+
+```bash
+# Make sure these are set (also documented in the main README)
+export LVAE_TIMESTAMP="2025.06.03.17.23.30"
+export LVAE_EPOCH="299"
+export LVAE_STEP="580200"
+
+# LVAE checkpoint
+LVAE_DIR=$PRITTI_EXP_ROOT/exp/training_lvae_model/training_lvae_model/$LVAE_TIMESTAMP/checkpoints
+mkdir -p $LVAE_DIR
+huggingface-cli download raniatze/pritti-checkpoints lvae.ckpt --local-dir $LVAE_DIR
+mv $LVAE_DIR/lvae.ckpt $LVAE_DIR/epoch=$LVAE_EPOCH-step=$LVAE_STEP.ckpt
+
+# LDM (DiT-B) checkpoint
+LDM_DIR=$PRITTI_EXP_ROOT/exp/training_dit_model/training_dit_b_model/training_dit_b_model/$LVAE_TIMESTAMP
+mkdir -p $LDM_DIR
+huggingface-cli download raniatze/pritti-checkpoints --include "ldm_b/*" --local-dir $LDM_DIR
+mv $LDM_DIR/ldm_b $LDM_DIR/checkpoint
+```
+
+Once downloaded, follow the [Inference](https://github.com/autonomousvision/pritti#-inference) section of the main README to reconstruct and generate scenes.
+
+## License
+
+Released under **CC BY-NC 4.0** — free for academic and non-commercial research use. See [LICENSE](https://github.com/autonomousvision/pritti/blob/main/LICENSE) for full terms.
+
+## Citation
+
+If you find PrITTI useful, please cite:
+
+```bibtex
+@inproceedings{Tze2026PrITTI,
+    author    = {Tze, Christina Ourania and Dauner, Daniel and Liao, Yiyi and Tsishkou, Dzmitry and Geiger, Andreas},
+    title     = {PrITTI: Primitive-based Generation of Controllable and Editable 3D Semantic Scenes},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year      = {2026},
+}
+```