Uni-Fusion: Universal Continuous Mapping

Yijun Yuan, Andreas Nüchter

Preprint | website

Uni-Fusion is nothing to do with NeRF!

It is a Fusion method (only forward and fusion)!

Universal encoder no need data train | Voxel grid for mapping

Therefore, it supports any mapping:

Table of Contents

1. Installation
2. Demo
3. TODO
4. Citation
5. Acknowledgement

Env setting and install

Unfold this for installation

• Create env

conda create -n uni python=3.8
conda activate uni

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
pip install torch-scatter torch-sparse torch-geometric -f https://data.pyg.org/whl/torch-1.12.0+cu113.html
pip install ninja functorch==0.2.1 numba open3d opencv-python trimesh

• install package

git clone https://github.com/Jarrome/Uni-Fusion.git && cd Uni-Fusion
# install uni package
python setup.py install
# install cuda function, this may take several minutes, please use `top` or `ps` to check
python uni/ext/__init__.py

• train a uni encoder from nothing in 1 second

python uni/encoder/uni_encoder_v2.py

optionally, you can install the [ORB-SLAM2](https://github.com/Jarrome/Uni-Fusion-use-ORB-SLAM2) that we use for tracking

cd external
git clone https://github.com/Jarrome/Uni-Fusion-use-ORB-SLAM2
cd [this_folder]
# this_folder is the absolute path for the orbslam2
# Add ORB_SLAM2/lib to PYTHONPATH and LD_LIBRARY_PATH environment variables
# I suggest putting this in ~/.bashrc
export PYTHONPATH=$PYTHONPATH:[this_folder]/lib
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:[this_folder]/lib

./build.sh && ./build_python.sh

Demo

0. Quick try

We provide a toy example to quick try our algorithm. You can either python example/toy.py or code as following:

import torch
import numpy as np

from example.util import get_modules, get_example_data

device = torch.device("cuda", index=0)

# get mapper and tracker
sm, cm, tracker, config = get_modules(device)

# prepare data
colors, depths, customs, calib, poses = get_example_data(device)

for i in [0, 1]:
    # preprocess rgbd to point cloud
    frame_pose = tracker.track_camera(colors[i], depths[i], customs, calib, poses[i], scene = config.sequence_type)
    # transform data
    tracker_pc, tracker_normal, tracker_customs= tracker.last_processed_pc
    opt_depth = frame_pose @ tracker_pc
    opt_normal = frame_pose.rotation @ tracker_normal
    color_pc, color, color_normal = tracker.last_colored_pc
    color_pc = frame_pose @ color_pc
    color_normal = frame_pose.rotation @ color_normal if color_normal is not None else None

    # mapping pc
    sm.integrate_keyframe(opt_depth, opt_normal)
    cm.integrate_keyframe(color_pc, color, color_normal)

# mesh extraction
map_mesh = sm.extract_mesh(config.resolution, int(4e7), max_std=0.15, extract_async=False, interpolate=True)

import open3d as o3d
o3d.io.write_triangle_mesh('example/mesh.ply', map_mesh)

You will get a mesh looks like this:

---

Then

All demo can be run with python demo.py [config]

1. Reconstruction Demo

# download replica data
source scripts/download_replica.sh

# with gt pose
python demo.py configs/replica/office0.yaml

# with slam
python demo.py configs/replica/office0_w_slam.yaml

• in [scene_w_slam.yaml], we can choose 3 mode

Usage	load_gt	slam
use SLAM track	False	True
use SLAM pred pose	True	True
use GT pose	True	False

• you can set vis=True for online vis (False by default), which is more Di-Fusion. You can tap keyboard ',' for step and '.' for continue running with GUI

• We will later provide a LIM_extraction given result LIMs

2. Custom context Demo

office0_custom.yaml contains all mapping you need

# if you need saliency
pip install transparent-background
# if you need style
cd external
git clone https://github.com/Jarrome/PyTorch-Multi-Style-Transfer.git
cd PyTorch-Style-Transfer/experiments
bash models/download_model.sh

# run demo
python demo.py configs/replica/office0_custom.yaml

3. Open Vocabulary Scene Understanding Demo

This Text-Visual CLIP is from OpenSeg

# install requirements
pip install tensorflow
pip install git+https://github.com/openai/CLIP.git

# download openseg ckpt
gsutil cp -r gs://cloud-tpu-checkpoints/detection/projects/openseg/colab/exported_model ./external/openseg/

python demo.py configs/replica/office0_w_clip.yaml

4. Self-captured data

Azure capturing

We provide the script to extract RGB, D and IR from azure.mp4: azure_process

---

TODO:

• Upload the uni-encoder src (Jan.3)
• Upload the env script (Jan.4)
• Upload the recon. application (By Jan.8)
• Upload the used ORB-SLAM2 support (Jan.8)
• Upload the azure process for RGB,D,IR (Jan.8)
• Upload the seman. application (Jan.14)
• Upload the Custom context demo (Jan.14)
• Toy example for fast essembling Uni-Fusion into custom project
• Extraction from Latent Implicit Maps (LIMs)
• Our current new project has a better option, I plan to replace this ORB-SLAM2 with that option after complete that work.

---

Citation

If you find this work interesting, please cite us:

@article{yuan2024uni,
  title={Uni-Fusion: Universal Continuous Mapping},
  author={Yuan, Yijun and N{\"u}chter, Andreas},
  journal={IEEE Transactions on Robotics},
  year={2024},
  publisher={IEEE}
}

Acknowledgement

• This implementation is on top of DI-Fusion.
• We also borrow some dataset code from NICE-SLAM.
• We thank the detailed response of questions from Kejie Li, Björn Michele, Songyou Peng and Golnaz Ghiasi.