Dekai Zhu*     Yixuan Hu*     Youquan Liu     Dongyue Lu     Lingdong Kong     Slobodan Ilic
(* Equal Contribution)
NeurIPS 2025

Existing LiDAR generative models are limited to producing unlabeled LiDAR scenes, lacking any semantic annotations. Performing post-hoc labeling on these generated scenes requires additional pretrained segmentation models, which introduces extra computational overhead. Moreover, such after-the-fact annotation yields suboptimal segmentation quality.

To address this issue, we make the following contributions:

• We propose a novel state-of-the-art semantic-aware range-view LiDAR diffusion model, Spiral, which jointly produces depth and reflectance images along with semantic labels.
• We introduce unified evaluation metrics that comprehensively evaluate the geometric, physical, and semantic quality of generated labeled LiDAR scenes.
• We demonstrate the effectiveness of the generated LiDAR scenes for training segmentation models, highlighting Spiral's potential for generative data augmentation.

If you find this work helpful for your research, please kindly consider citing our paper:

@inproceedings{zhu2025spiral,
    title     = {Spiral: Semantic-Aware Progressive LiDAR Scene Generation and Understanding},
    author    = {Zhu, Dekai and Hu, Yixuan and Liu, Youquan and Lu, Dongyue and Kong, Lingdong and Ilic, Slobodan},
    booktitle = {The Thirty-ninth Annual Conference on Neural Information Processing Systems},
    year      = {2025}
}

• [11/2025] - The code for Spiral is released. 🚀
• [10/2025] - The project page is online. 🚀
• [09/2025] - This work has been accepted to NeurIPS 2025.

For details related to installation and environment setups, please run:

conda env create -f environment.yaml
conda activate spiral

If you are stuck with an endless installation, try:

mamba env create -f environment.yaml
conda activate spiral

We use the official SemanticKITTI API to preprocess the data by projecting the LiDAR data from Cartesian coordinates into range images. You can download the preprocessed data here. 🤗

First, specify the data_path in utils/option.py to point to the directory of the preprocessed data. Then simply run:

python train.py

to start the training.

This work is developed based on the R2DM codebase.