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Running
on
Zero
| ## Tanks and Temples. | |
| ### Image set | |
| 1. Data Preparation | |
| Download the images data from [here](https://cvg.cit.tum.de/data/datasets/rgbd-dataset/download) (for intermidiated and advanced set, please download from [here](https://github.com/isl-org/TanksAndTemples/issues/35)), and COLMAP results from (here)[https://storage.googleapis.com/niantic-lon-static/research/acezero/colmap_raw.tar.gz]. We thank [ACE0](https://github.com/nianticlabs/acezero) again for providing the COLMAP results. | |
| 2. Adjust the parameter in `run_tnt.sh` | |
| Specify the `dataset_root`, `colmap_dir`, `model_path` and `save_dir` in the file. | |
| 3. Get the inference results. | |
| ``` | |
| sh run_tnt.sh | |
| ``` | |
| ### Video set | |
| <details> | |
| <summary>Click to expand</summary> | |
| 1. Data Preparation | |
| Download the video sequence and from [here](https://www.tanksandtemples.org/download/) and get images from video via [this](https://www.tanksandtemples.org/tutorial/). | |
| 2. Run Inference | |
| Replace `docs/demo_image` in `../demo.py` to the path storing images from videl. | |
| </details> | |
| ## 7 scenes | |
| 1. Data Preparation | |
| Download the corresponding sequence from [here](https://jonbarron.info/mipnerf360/). | |
| ## TUM-RGBD | |
| 1. Data Preparation | |
| Download the corresponding sequence from [here](https://cvg.cit.tum.de/data/datasets/rgbd-dataset/download). | |
| 2. Adjust the parameter in `run_tum.sh` | |
| Specify the `dataset_root`, `recon_img_num`, `model_path` and `save_dir` in the file. | |
| 3. Evaluate the results. | |
| ``` | |
| sh run_tum.sh | |
| ``` | |
| Noting that we set the `recon_img_num` to 50 or 100 according to the length of dataset. Please refer to the supplementary of paper for detail. | |
| 4. Using evo to evaluate The results | |
| ``` | |
| evo_ape tum gt_pose.txt pred_tum.txt -vas | |
| ``` | |
| ## 7 scenes | |
| 1. Download the dataset from [here](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/) and _Pseudo Ground Truth (PGT)_ | |
| (see | |
| the [ICCV 2021 paper](https://openaccess.thecvf.com/content/ICCV2021/html/Brachmann_On_the_Limits_of_Pseudo_Ground_Truth_in_Visual_Camera_ICCV_2021_paper.html) | |
| , | |
| and [associated code](https://github.com/tsattler/visloc_pseudo_gt_limitations/) for details). | |
| 2. Adjust the parameter in `run_7scenes.sh` | |
| Specify the `dataset_root`, `recon_img_num`, `model_path` and `save_dir` in the file. | |
| 3. Evaluate the results. | |
| ``` | |
| sh run_7scenes.sh | |
| ``` | |
| You will see a `result.txt` file reporting the evaluation results. | |
| ## Mip-NeRF 360 | |
| 1. Data Preparation | |
| Download the data from [here](https://jonbarron.info/mipnerf360/). | |
| 2. Adjust the parameter in `run_mip.sh` | |
| Specify the `dataset_root`, `model_path` and `save_dir` in the file. | |
| 3. Get the inference results. | |
| ``` | |
| sh run_mip.sh | |
| ``` | |
| ## Co3D-V2 | |
| 1. We thank VGGT for providing evaluation code of CO3D-V2 dataset. Please see link [here](https://github.com/facebookresearch/vggt/tree/evaluation/evaluation#dataset-preparation) for data preparation and processing. | |
| 2. Adjust the parameterco3d_dir in `runco3d_anno_dir_7scenes.sh` | |
| Specify the `dataset_root`, `recon_img_num`, `model_path`, `recon`, `reloc` and `fixed_rank` in the file. | |
| 3. Evaluate the results. | |
| ``` | |
| sh run_co3d.sh | |
| ``` | |
| You will see evaluation result in the terminal. | |