---
license: mit
pipeline_tag: other
tags:
- 3D point clouds
- reconstruction
- ply
library_name: MinkowskiEngine
---

# **ScoreLiDAR: Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion**

[📚 Paper](https://arxiv.org/abs/2412.03515) - [💻 Code Repository](https://github.com/happyw1nd/ScoreLiDAR)

## Abstract
Diffusion models have been applied to 3D LiDAR scene completion due to their strong training stability and high completion quality. However, the slow sampling speed limits the practical application of diffusion-based scene completion models since autonomous vehicles require an efficient perception of surrounding environments. This paper proposes a novel distillation method tailored for 3D Li- DAR scene completion models, dubbed ScoreLiDAR, which achieves efficient yet high-quality scene completion. Score- LiDAR enables the distilled model to sample in significantly fewer steps after distillation. To improve completion quality, we also introduce a novel Structural Loss, which encourages the distilled model to capture the geometric structure of the 3D LiDAR scene. The loss contains a scene-wise term constraining the holistic structure and a point-wise term constraining the key landmark points and their relative configuration. Extensive experiments demonstrate that ScoreLiDAR significantly accelerates the completion time from 30.55 to 5.37 seconds per frame (>5x) on SemanticKITTI and achieves superior performance compared to state-of-the-art 3D LiDAR scene completion models. Our model and code are publicly available on https: //github.com/happyw1nd/ScoreLiDAR.

## Repo Info
This repository stores checkpoints for the ScoreLiDAR paper. For more information and detailed usage of the models, please refer to the [official GitHub repository](https://github.com/happyw1nd/ScoreLiDAR).

## Sample Usage

To get started with ScoreLiDAR, follow these steps to set up the environment, download the necessary weights, and run an inference example.

1.  **Clone the repository:**
    ```bash
    git clone https://github.com/happyw1nd/ScoreLiDAR
    cd ScoreLiDAR
    ```

2.  **Install the environment:**
    ```bash
    sudo apt install build-essential python3-dev libopenblas-dev
    pip3 install -r requirements.txt
    pip3 install -U MinkowskiEngine==0.5.4 --install-option="--blas=openblas" -v --no-deps
    pip3 install -U -e .
    ```

3.  **Download model checkpoints:**
    Download the required checkpoint files from this Hugging Face repository and place them in the `checkpoints/` directory within your cloned `ScoreLiDAR` repository.
    ```bash
    huggingface-cli download happywind/ScoreLiDAR ScoreLiDAR_diff_net.ckpt --local-dir checkpoints
    huggingface-cli download happywind/ScoreLiDAR refine_net.ckpt --local-dir checkpoints
    ```

4.  **Run inference:**
    The inference script will read `.ply` files from the `scorelidar/Datasets/test/` directory. A sample `.ply` file is typically included in the repository for testing. Results will be saved under `scorelidar/results/`.
    ```bash
    python3 tools/diff_completion_pipeline.py --denoising_steps 8 --cond_weight 3.5
    ```

5.  **Visualize results (optional):**
    You can visualize the generated point cloud files using the provided script:
    ```bash
    python3 vis_pcd.py -p <path_to_.ply_file>
    ```

## Citation
If you find our paper useful or relevant to your research, please kindly cite our papers:

```bibtex
@article{zhang2024distillingdiffusionmodels,
    title={Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion}, 
    author={Shengyuan Zhang and An Zhao and Ling Yang and Zejian Li and Chenye Meng and Haoran Xu and Tianrun Chen and AnYang Wei and Perry Pengyun GU and Lingyun Sun,
    journal={arXiv:2412.03515},
    year={2024}
}
```