Photo-SLAM Evaluation Toolkit

Homepage | Paper

Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras
Huajian Huang¹, Longwei Li², Hui Cheng², and Sai-Kit Yeung¹
The Hong Kong University of Science and Technology¹, Sun Yat-Sen University²
In Proceedings of Computer Vision and Pattern Recognition Conference (CVPR), 2024

Prerequisites

To use this toolkit, you have to ensure your results on each dataset are stored in the correct format. For example,

results
├── replica_mono_0
│   ├── office0
│   ├── ....
│   └── room2
├── replica_rgbd_0
│   ├── office0
│   ├── ....
│   └── room2
│
└── [replica/tum/euroc]_[mono/stereo/rgbd]_num  ....
    ├── scene_1
    ├── ....
    └── scene_n

Install required python packages

# You need to install a compatible Pytorch as well.
# such as conda install pytorch==2.1.2 torchvision==0.16.2 pytorch-cuda=11.8 -c pytorch -c nvidia

git clone https://github.com/HuajianUP/Photo-SLAM-eval.git
pip install evo numpy scipy scikit-image lpips pillow tqdm plyfile

(Optional) Install submodel for rendering

# If you have installed original GS submodels, you can skip these steps.

pip install submodules/simple-knn/ 
pip install submodules/diff-gaussian-rasterization/

Convert Replica GT camera pose files to suitable pose files to run the EVO package

python shapeReplicaGT.py --replica_dataset_path PATH_TO_REPLICA_DATASET

Copy TUM camera.yaml to the corresponding dataset path

Since images on some sequences of TUM dataset contain distortion, we need to undistort the ground truth images before evaluation. In addition, the file camera.yaml is used as an indicator in run.py.

cp TUM/fr1/camera.yaml PATH_TO_TUM_DATASET/rgbd_dataset_freiburg1_desk
cp TUM/fr2/camera.yaml PATH_TO_TUM_DATASET/rgbd_dataset_freiburg2_xyz

Evaluation

To get all the metrics, you can run

python onekey.py --dataset_center_path PATH_TO_ALL_DATASET --result_main_folder RESULTS_PATH

Finally, you are supposed to get two files including RESULTS_PATH/log.txt and RESULTS_PATH/log.csv.