Awesome
RangeLDM
[ECCV 2024] Official implementation of "RangeLDM: Fast Realistic LiDAR Point Cloud Generation"
Models
KITTI360
| Model | MMD | FRD | JSD | Checkpoint | Generated Point Clouds |
|---|---|---|---|---|---|
| RangeLDM | 3.07 × 10^−5 | 1074.9 | 0.045 | [PKU Disk]<br/>(115MB) | [1k samples] |
| RangeDM | 4.14 × 10^−5 | 899.0 | 0.040 | [PKU Disk]<br/>(401MB) | [1k samples] |
nuScenes
| Model | MMD | JSD | Checkpoint | Generated Point Clouds |
|---|---|---|---|---|
| RangeLDM | 1.9 × 10^−4 | 0.054 | [PKU Disk]<br/>(153MB) | [1k samples] |
Train
VAE
cd vae
python main.py --base configs/kitti360.yaml
LDM
cd ldm
accelerate launch train_unconditional.py --cfg configs/RangeLDM.yaml # for unconditional generation
accelerate launch train_conditional.py --cfg configs/upsample.yaml # for conditional generation
Evaluation
Citation
If you find our work useful, please cite:
@article{hu2024rangeldm,
title={RangeLDM: Fast Realistic LiDAR Point Cloud Generation},
author={Hu, Qianjiang and Zhang, Zhimin and Hu, Wei},
journal={arXiv preprint arXiv:2403.10094},
year={2024}
}