R2Flow

June 29, 2025 · View on GitHub

  • This is the official implementation of our ICRA 2025 paper, "Fast LiDAR Data Generation with Rectified Flows".
  • R2Flow is a rectified flow-based LiDAR generative model, which can generate LiDAR data in few steps.

https://github.com/user-attachments/assets/5fdb9469-799b-438f-8334-a16d5e8180f8

Fast LiDAR Data Generation with Rectified Flows
Kazuto Nakashima, Xiaowen Liu, Tomoya Miyawaki, Yumi Iwashita, Ryo Kurazume
ICRA 2025
Project page | arXiv | Demo

Setup

Dataset (optional)

For training and evaluation, download a KITTI-360 dataset (163 GB) and set an environment variable ${KITTI360_ROOT}:

export KITTI360_ROOT=<path to the KITTI-360 dataset>

KITTI360_ROOT
├── data_3d_raw/
│   ├── 2013_05_28_drive_0000_sync
│   └── ...
└── data_3d_semantics/
    ├── 2013_05_28_drive_0000_sync
    └── ...

Dependencies

Clone this repository:

git clone https://github.com/kazuto1011/r2flow.git
cd r2flow

With Docker & NVIDIA Container Toolkit (recommended for reproducibility):

docker compose up --detach
docker compose exec r2flow bash
Without Docker: 
ln -sf $KITTI360_ROOT ./r2flow/data/kitti_360/dataset
pip install -r requirements.txt
sudo apt install libsparsehash-dev # for torchsparse
pip install git+https://github.com/mit-han-lab/torchsparse.git@v2.0.0
pip install natten==0.17.1+torch210cu121 --find-links https://shi-labs.com/natten/wheels/

Quick demo

Unconditional generation using the pre-trained model:

import torch
import torchdiffeq

# Settings
torch.set_grad_enabled(False)
model_config = "r2flow-kitti360-1rf"  # See https://github.com/kazuto1011/r2flow/releases/tag/weights
nfe = 256  # Number of function evaluations
batch_size = 1
device = "cuda"

# Setup a pre-trained model
model, lidar_utils, cfg = torch.hub.load(
    repo_or_dir="kazuto1011/r2flow", model="pretrained_r2flow", config=model_config, device=device
)

# Euler sampling
t = torch.linspace(0, 1, nfe + 1, device=device)
x0 = torch.randn(batch_size, model.in_channels, *model.resolution, device=device)
x1 = torchdiffeq.odeint(func=model, y0=x0, t=t, method="euler")[-1]

# Post-processing
range_image = lidar_utils.restore_metric_depth(x1[:, [0]])  # range in [0, 80]
rflct_image = lidar_utils.denormalize(x1[:, [1]])  # reflectance in [0, 1]
point_cloud = lidar_utils.convert_metric_depth(range_image, format="cartesian")

We also provide a Gradio-based demo on Hugginface spaces.

Training & evaluation

See this page about how to reproduce R2Flow.

Citation

If you find this code useful for your research, please cite our paper:

@inproceedings{nakashima2025fast,
    title     = {Fast {LiDAR} Data Generation with Rectified Flows},
    author    = {Kazuto Nakashima and Xiaowen Liu and Tomoya Miyawaki and Yumi Iwashita and Ryo Kurazume},
    booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
    pages     = {10057--10063},
    year      = 2025
}

Acknowledgements