EGSRAL

KO | EN

gitlite — search

#python #react #ai #typescript #go #android #machine-learning #rust #docker #nodejs #cli #awesome-list #vue #security

(★ 25)

EGSRAL: An Enhanced 3D Gaussian Splatting based Renderer with Automated Labeling for Large-Scale Driving Scene

EGSRAL Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# EGS-RAL：An Enhanced 3D Gaussian Splatting based Renderer with Automated Labeling for Large-Scale Driving Scene
This repository contains the official implementation associated with the paper "EGSRAL:An Enhanced 3D Gaussian Splatting Based Renderer with Automated Labeling for Large-Scale Driving Scene".

⭐ [Arxiv](https://arxiv.org/abs/2412.15550)

> Yixiong Huo\*, Guangfeng Jiang\*, Hongyang Wei, Ji Liu†, Song Zhang, Han Liu, Xingliang Huang, Mingjie Lu, Jinzhang Peng, Dong Li, Lu Tian, Emad Barsoum  
> AAAI 2025

![framework](https://raw.githubusercontent.com/jiangxb98/EGSRAL/main/images/egsral.png)

## ToDo

- [ ] The code is being approved internally.

## Dataset

In our paper, we use:

- KITTI City used in READ paper.
- NuScenes-S [164, 209, 359,916] used in S-NeRF paper.
- NuScenes-D [103 168 212 220 228 687] used in DrivingGaussian paper.

These datasets generated by the huo, yixiong.

- KITTI `dataset/KITTI`
- NuScenes-S `dataset/datasets_s`
- NuScenes-D `dataset/datasets_d`

> Note: If use the  NuScenes-D please use the python script `3dgs-utils/sort_rename_dgs.py` to generate the id map pkl file·

## Run

### Environment

> Baed on the [Deformable 3DGS](https://github.com/ingra14m/Deformable-3D-Gaussians).

```bash
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
python -m pip install setuptools==69.5.1
pip install plyfile tqdm opencv-python 
cd submodules
pip install -e depth-diff-gaussian-rasterization
pip install -e simple-knn
```

### Train

#### **KITTI-City**

```bash
# train 3dgs
CUDA_VISIBLE_DEVICES=0 python train_kitti_3dgs.py -s dataset/KITTI -m logs/kitti_3dgs --data_device cuda --eval --port 6040
# render
CUDA_VISIBLE_DEVICES=0 python render_3dgs.py -m logs/kitti_3dgs --skip_train --data_device cuda --mode render --iteration 9999999 --scene_name kitti18
# metric
CUDA_VISIBLE_DEVICES=0 python metrics_alex.py -m logs/kitti_3dgs

# train deformable 3dgs
CUDA_VISIBLE_DEVICES=0 python train_kitti_d3dgs.py -s dataset/KITTI -m logs/kitti_d3dgs --data_device cuda --eval --port 6040
CUDA_VISIBLE_DEVICES=0 python render_d3dgs.py -m logs/kitti_d3dgs --skip_train --data_device cuda --mode render --iteration 9999999 --scene_name kitti18
CUDA_VISIBLE_DEVICES=0 python metrics_alex.py -m logs/kitti_d3dgs

# train EGSRAL without group
CUDA_VISIBLE_DEVICES=0 python train_kitti_egsral.py -s dataset/KITTI -m logs/kitti_egsral --data_device cuda --eval --port 6047
CUDA_VISIBLE_DEVICES=0 python render_egsral.py -m logs/kitti_egsral --skip_train --data_device cuda --mode render --iteration 9999999 --scene_name kitti18
CUDA_VISIBLE_DEVICES=0 python metrics_alex.py -m logs/kitti_egsral

# train EGSRAL with group
CUDA_VISIBLE_DEVICES=4,5,6,7 python train_kitti_egsral_group.py -s dataset/KITTI -m logs/kitti_egsral_group --data_device cuda --eval --port 6047
CUDA_VISIBLE_DEVICES=0 python render_egsral_group.py -m logs/kitti_egsral_group --skip_train --data_device cuda --mode render --iteration 9999999 --scene_name kitti18
CUDA_VISIBLE_DEVICES=0 python metrics_alex.py -m logs/kitti_egsral_group
```

## Experiment Results

### KITTI-City

### NuScenes-D

### NuScenes-S

## Visualization

Qualitative comparison of novel view synthesis on the KITTI City dataset.

Visualizing 3D auto labeling on nuScenes.

## Acknowledgments

We sincerely thank the authors of [3DGS](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) and [Deformable 3DGS](https://github.com/ingra14m/Deformable-3D-Gaussians), whose codes were used in our work.

EGSRAL

EGSRAL Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

EGSRAL

EGSRAL Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README