ru4dslam

KO | EN

gitlite — search

#golang #machine-learning #docker #nodejs #macos #java #awesome #cli #vue #security #pytorch #mcp #openai #open-source

(★ 17)

[CVPR Findings 2026] RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction

ru4dslam 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

<h1 align="center">RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction</h1>
  <p align="center">
    <span class="author-block">
                <a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Yangfan Zhao</a><sup>1*</sup></span>
                <span class="author-block">
                  <a href="https://scholar.google.com/citations?user=7QjjN2wAAAAJ&hl=en" target="_blank">Hanwei Zhang</a><sup>2*</sup></span>
                  <span class="author-block">
                    <a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Ke Huang</a><sup>3</sup>
                  </span>
                  <br>
                  <span class="author-block">
                    <a href="https://scholar.google.com/citations?user=qj66yXAAAAAJ&hl=en" target="_blank">Qiufeng Wang</a><sup>3</sup>
                  </span>
                  <span class="author-block">
                    <a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Zhenzhou Shao</a><sup>1&dagger;</sup>
                  </span>
                  <span class="author-block">
                    <a href="https://scholar.google.com/citations?user=-Ji_VmkAAAAJ&hl=en" target="_blank">Dengyu Wu</a><sup>4&dagger;</sup>
                  </span>
                  <span class="eql-cntrb"><small><br><sup>*</sup>Equal contribution</small><small>&nbsp;&nbsp;&nbsp;&nbsp;<sup>&dagger;</sup>Corresponding Author</small></span>
</p>
<p align="center"> <strong>CVPR Findings 2026</strong></p>
  <h3 align="center"><a href="https://openaccess.thecvf.com/content/CVPR2026F/html/Zhao_RU4D-SLAM_Reweighting_Uncertainty_in_Gaussian_Splatting_SLAM_for_4D_Scene_CVPRF_2026_paper.html">Paper</a> | <a href="https://ru4d-slam.github.io">Project Page</a> | <a href="https://arxiv.org/abs/2602.20807">arXiv</a></h3>
  <div align="center"></div>
</p>
<p align="center">
    <img src="./.media/method_v260228.png" alt="teaser_image" width="100%">
</p>
<p style="text-align: justify; max-width: 800px; line-height: 1.5;">
<b>Overview of RU4D-SLAM</b>. 
RU4D-SLAM operates in three stages: pose estimation, deformation field initialization, and 4D mapping, all of which are closely linked to the uncertainty map <i>β²</i>. 
In the pose estimation stage, <i>β²</i> supports uncertainty-aware DBA tracking. 
Before 4D mapping, the uncertainty map is combined with SAM to form RUM, within which deformation nodes are initialized as local motion anchors by a pretrained SpaTracker model.
In 4D mapping, node trajectories are propagated to Gaussians via deformation blending and optimized through IR- and AOW-guided training for joint static and dynamic rendering at each keyframe.
Snowflake icons denote pre-trained, frozen modules.
</p>
<p style="text-align: center; margin-top: 10px;">
</p>
<br>

## Results

<details>
<summary><font size="4">
TUM Rendering Quality Results
</font></summary>
<div align="center">
<img src="./.media/TUM_result.png" width="60%">
</div>
</details>

<details>
<summary><font size="4">
Bonn Rendering Quality Results
</font></summary>
<div align="center">
<img src="./.media/Bonn_result.png" width="60%">
</div>
</details>

<details>
<summary><font size="4">
Wild-SLAM Rendering Quality Results
</font></summary>
<div align="center">
<img src="./.media/Wild-SLAM_result.png" width="60%">
</div>
</details>

<details>
<summary><font size="4">
Modules Ablation
</font></summary>
<div align="center">
<img src="./.media/Modules_ablation.png" width="60%">
</div>
</details>

<details>
<summary><font size="4">
IR Ablation
</font></summary>
<div align="center">
<img src="./.media/IR_ablation.png" width="60%">
</div>
</details>

## Install

1. Simply run the following command. This script assumes you have an Ubuntu environment and Anaconda installed. The Pytorch version used is 2.1.2 and CUDA version is 12.1. You may have to tweak the script to fit your own environment. For the download link of package Pytorch3D in ```install.sh```, please modify refering to [here](https://api.anaconda.org/download/pytorch3d/pytorch3d/) to fit your own environment.
    ```bash
    source install.sh
    ```

2. Download from [here](https://drive.google.com/file/d/15tveiv7ZkvBBAN3qkkB7Zfky9d7vSqLD/view?usp=sharing) some checkpoints for the 2D foundational models if they are not HG downloadables.

WARNING: By downloading these checkpoints, you must agree and obey the original license from the original authors ([RAFT](https://github.com/princeton-vl/RAFT), [SpaTracker](https://github.com/henry123-boy/SpaTracker), and [TAPNet](https://github.com/google-deepmind/tapnet)). Unzip the weights into the following file structure:
    ```bash
    ProjRoot/weights
    ├── raft_models
    │   ├── raft-things.pth
    │   └── ...
    ├── spaT_final.pth
    └── tapnet
        └── bootstapir_checkpoint_v2.pt
    ```

3. Download the pretained models [droid.pth](https://drive.google.com/file/d/1PpqVt1H4maBa_GbPJp4NwxRsd9jk-elh/view?usp=sharing), put it inside the `pretrained` folder.
   
## Run

You can download the different datasets by the following command. 
```bash
bash scripts_downloading/download_tum.sh
bash scripts_downloading/download_bonn.sh
bash scripts_downloading/download_wild_slam_iphone.sh
```
Then, you can run RU4D-SLAM to test all sequences via the following command:
```bash
bash scripts_run/run_tum_dynamic_all.sh 
bash scripts_run/run_bonn_all.sh
bash scripts_run/run_wild_slam_iphone_all.sh
```

If you want just to run a single sequence, you can run the following commands to run each part of pipelines like:
```bash
python run.py ./configs/Dynamic/TUM_RGBD/freiburg3_walking_rpy.yaml
python gen_rum.py ./configs/Dynamic/TUM_RGBD/freiburg3_walking_rpy.yaml
python precompute_4dgs.py --cfg ./configs/Dynamic/TUM_RGBD/freiburg3_walking_rpy.yaml --ws test
python reconstruct_4dgs.py --cfg ./configs/Dynamic/TUM_RGBD/freiburg3_walking_rpy.yaml --ws test
```

## Citation

If you find our code or paper useful, please cite
```bibtex
@InProceedings{Zhao_2026_CVPR,
    author    = {Zhao, Yangfan and Zhang, Hanwei and Huang, Ke and Wang, Qiufeng and Shao, Zhenzhou and Wu, Dengyu},
    title     = {RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings},
    month     = {June},
    year      = {2026},
    pages     = {1492-1502}
}
```

ru4dslam

ru4dslam 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

ru4dslam

ru4dslam 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README