This project is based on our SIGGRAPH 2021 paper, ROSEFusion: Random Optimization for Online DenSE Reconstruction under Fast Camera Motion .

ROSEFusion is proposed to tackle the difficulties in fast-motion camera tracking using random optimization with depth information only. Our method performs robust camera tracking under fast camera motion at a real-time frame rate, without loop closure or global pose optimization.

Our code is based on C++ and CUDA with the support of:

• Pangolin (tested on v0.6)
• OpenCV with CUDA (v.4.5 is required, for instance you can follow the link)
• Eigen (tested on 3.3.9)
• CUDA (tested with V11.1, 11.4)

The code has been tested with Nvidia GeForce RTX 2080 SUPER on Ubuntu 16.04.

Our code is based on C++ and CUDA with the support of:

• Pangolin (tested on v0.6)
• OpenCV with CUDA (v.4.5.5 is required, for instance you can follow the link)
• Eigen (tested on 3.4.0)
• CUDA (tested with V11.8)

The code has been tested with Nvidia GeForce RTX 3060 SUPER on Ubuntu 20.04.

Please make sure the architecture (sm_xx and compute_xx) in the L22 of CMakeLists.txt is compatible with your own graphics card.

We have already upload a docker image with all the lib, code and data. You can download the docker image from the One drive.

Please make sure you have successfully installed the docker and nvidia docker. and once the environment is ready, you can use following commands to boot the docker image:

sudo docker load -i rosefusion_docker.tar 
sudo docker run -it  --gpus all jiazhao/rosefusion:v7 /bin/bash

And please check the architecture in the L22 of /home/code/ROSEFusion-main/CMakeList.txt is compatible with your own graphics card. If not, change the sm_xx and compute_xx, then rebuild the ROSEFusion.

We have already configured the path and data in the docker image. You can simply run "run_example.sh" and "run_stairwell.sh" at /home/code/ROSEFusion-main/build and the trajectory and reconstuciton would be saved in /home/code/rosefusion_xxx_data.

We use the following configuration files to make the parameters setting easier. There are four types of configuration files.

• seq_generation_config.yaml: data information
• camera_config.yaml: camera and image information.
• data_config.yaml: output path, sequence file path and parameters of the volume.
• controller_config.yaml: visualization, results saving and parameters of tracking.

The seq_generation_config.yaml is only used for data preparation, and the other three types of configuration files are necessary to run the ROSEFusion. We have alreay prepared some configuration files of some common datasets, you can check the details in [type]_config/ directory. You can change the parameters to fit your own dataset.

The details of data preparation can be found in src/seq_gen.cpp. By using the seq_generation_config.yaml introduced above, you can run the script as:

./seq_gen  sequence_information.yaml

Once finished, there would be a .seq file which could be used for future reconstruction.

We share the same pre-sampled PST as our paper. Each PST is saved as an N×6 image and the N means the number of particles. You can find the .tiff images in PST dicrectory, and please change the PST path in controller_config.yaml with your own path.

Finally, to run the ROSEFusion, you need to provide the camera_config.yaml, data_config.yaml and controller_config.yaml. We already share configuration files of many common datasets in ./camera_config, ./data_config, /controller_config. All the parameters of configuration files can be modified as you want. Once you have all the required files, you can run the ROSEFsuion as:

./ROSEFsuion  your_camera_config.yaml your_data_config.yaml your_controller_config.yaml

For a quick start, you can download and use a small size synthesis seq file with related configuration files. Here is a preview.

We present the Fast Camera Motion dataset, which contains both synthetic and real captured sequences. For more details, please refer to the paper.

With 10 diverse room-scale scenes from Replica Dataset, we render the color images and depth maps along the synthetic trajectories. The raw sequences are provided in FastCaMo-synth-data(raw).zip, and we also provide the FastCaMo-synth-data(noise).zip with synthetic noise and motion blur. We use the same noise model as simkinect. For evaluation, you can download the ground truth trajectories.

It contains 12 real captured RGB-D sequences under fast camera motions. Each sequence is recorded in a challenging scene like gym or stairwell by using Azure Kinect DK. We provide accurate dense reconstructions as ground truth, which are modeled with the high-end laser scanner. However, the original models are extremely large, and we utilized the built-in spatial downsample algorithm from cloudcompare. You can download the sub-sampled models of FastCaMo-real form here.

If you find our work useful in your research, please consider citing:

@article {zhang_sig21,
    title = {ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion},
    author = {Jiazhao Zhang and Chenyang Zhu and Lintao Zheng and Kai Xu},
    journal = {ACM Transactions on Graphics (SIGGRAPH 2021)},
    volume = {40},
    number = {4},
    year = {2021}
}

Q: The Frame could not be processed error is reported when running the example data.

A: Please make sure you have correctly installed the environment:

• Check the ompute_xx,code=sm_xx and make sure it is valid for your GPU device.
• Build the opencv with -D WITH_CUDA=ON and make sure the -D CUDA_ARCH_BIN=x.x is valid for your GPU device.

Our code is inspired by KinectFusionLib.

This is an open-source version of ROSEFusion, some functions have been rewritten to avoid certain license. It would not be expected to reproduce the result exactly, but the result is almost the same.

The source code is released under GPLv3 license.

If you have any questions, feel free to email Jiazhao Zhang at zhngjizh@gmail.com.