--- library_name: diffusers license: cc-by-nc-4.0 pipeline_tag: other tags: - 3d-scene-generation - latent-diffusion - autonomous-driving - kitti-360 - primitives - cvpr-2026 --- # PrITTI: Primitive-based Generation of Controllable and Editable 3D Semantic Urban Scenes 📄 Paper | 🌐 Project Page | 💻 Code This repository hosts the **pre-trained checkpoints** for **PrITTI** (CVPR 2026), a latent-diffusion framework for controllable and editable 3D semantic urban scene generation. Existing approaches to 3D semantic urban scene generation predominantly rely on voxel-based representations. In contrast, PrITTI advocates for a primitive-based paradigm where urban scenes are represented using compact, semantically meaningful 3D elements that are easy to manipulate and compose. PrITTI achieves state-of-the-art 3D scene generation quality with lower memory requirements and faster inference than voxel-based methods. ## Released Checkpoints The checkpoints below were trained on [KITTI-360](https://www.cvlibs.net/datasets/kitti-360/). | 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}, } ```
