深度学习三维重建 PatchMatchNet-CVPR-2021（源码、原文、注释、译文、批注）

# PatchmatchNet (CVPR2021 Oral) official source code of paper 'PatchmatchNet: Learned Multi-View Patchmatch Stereo'  ## Updates - 13.12.2021: New unified format for training and evaluation datasets, support for arbitrary image sizes and multi-camera setups, and new names for script parameters. - 27.09.2021: The code now allows for Torchscript export and includes a pre-trained TorchScript module. ## Introduction PatchmatchNet is a novel cascade formulation of learning-based Patchmatch which aims at decreasing memory consumption and computation time for high-resolution multi-view stereo. If you find this project useful for your research, please cite: ``` @misc{wang2020patchmatchnet, title={PatchmatchNet: Learned Multi-View Patchmatch Stereo}, author={Fangjinhua Wang and Silvano Galliani and Christoph Vogel and Pablo Speciale and Marc Pollefeys}, journal={CVPR}, year={2021} } ``` ## Installation ### Requirements * python 3.8 * CUDA >= 10.1 ``` pip install -r requirements.txt ``` ## Reproducing Results * Download our pre-processed dataset: [DTU's evaluation set](https://drive.google.com/file/d/1jN8yEQX0a-S22XwUjISM8xSJD39pFLL_/view?usp=sharing), [Tanks & Temples](https://drive.google.com/file/d/1gAfmeoGNEFl9dL4QcAU4kF0BAyTd-r8Z/view?usp=sharing) and [ETH3D benchmark](https://polybox.ethz.ch/index.php/s/pmTGWobErOnhEg0). Each dataset is already organized as follows: ``` root_directory ├──scan1 (scene_name1) ├──scan2 (scene_name2) ├── images │ ├── 00000000.jpg │ ├── 00000001.jpg │ └── ... ├── cams │ ├── 00000000_cam.txt │ ├── 00000001_cam.txt │ └── ... └── pair.txt ``` Note: - The subfolders for Tanks & Temples and ETH3D will not be named `scanN` but the lists included under `./lists/eth3d` and `./lists/tanks` will have the correct naming conventions. - If the folders for images and cameras, and the pair file don't follow the standard naming conventions you can modify the settings of `MVSDataset` in `datasets/mvs.py` to specify the custom `image_folder`, `cam_folder`, and `pair_path` - The `MVSDataset` is configured by default for JPEG images. If you're using a different format (e.g., PNG) you can change the `image_extension` parameter of `MVSDataset` accordingly. Camera file `cam.txt` stores the camera parameters, which includes extrinsic, intrinsic, minimum depth and maximum depth: ``` extrinsic E00 E01 E02 E03 E10 E11 E12 E13 E20 E21 E22 E23 E30 E31 E32 E33 intrinsic K00 K01 K02 K10 K11 K12 K20 K21 K22 DEPTH_MIN DEPTH_MAX ``` `pair.txt ` stores the view selection result. For each reference image, N (10 or more) best source views are stored in the file: ``` TOTAL_IMAGE_NUM IMAGE_ID0 # index of reference image 0 10 ID0 SCORE0 ID1 SCORE1 ... # 10 best source images for reference image 0 IMAGE_ID1 # index of reference image 1 10 ID0 SCORE0 ID1 SCORE1 ... # 10 best source images for reference image 1 ... ``` * In `eval.sh`, set `DTU_TESTING`, `ETH3D_TESTING` or `TANK_TESTING` as the root directory of corresponding dataset and uncomment the evaluation command for corresponding dataset (default is to evaluate on DTU's evaluation set). If you want to change the output location (default is same as input one), modify the `--output_folder` parameter. For Tanks the `--scan_list` can be intermediate or advanced and for ETH3D it can be test or train. * `CKPT_FILE` is the checkpoint file (our pretrained model is `./checkpoints/params_000007.ckpt`), change it if you want to use your own model. If you want to use the model from the TorchScript module instead, you can specify the checkpoint file as `./checkpoints/module_000007.pt` and set the option `--input_type module`. * Test on GPU by running `sh eval.sh`. The code includes depth map estimation and depth fusion. The outputs are the point clouds in `ply` format. * For quantitative evaluation on DTU dataset, download [SampleSet](http://roboimagedata.compute.dtu.dk/?page_id=36) and [Points](http://roboimagedata.compute.dtu.dk/?page_id=36). Unzip them and place `Points` folder in `SampleSet/MVS Data/`. The structure looks like: ``` SampleSet ├──MVS Data └──Points ``` In `evaluations/dtu/BaseEvalMain_web.m`, set `dataPath` as path to `SampleSet/MVS Data/`, `plyPath` as directory that stores the reconstructed point clouds and `resultsPath` as directory to store the evaluation results. Then run `evaluations/dtu/BaseEvalMain_web.m` in matlab. The results look like: | Acc. (mm) | Comp. (mm) | Overall (mm) | |-----------|------------|--------------| | 0.427 | 0.277 | 0.352 | * For detailed quantitative results on Tanks & Temples and ETH3D, please check the leaderboards ([Tanks & Temples](https://www.tanksandtemples.org/details/1170/), [ETH3D](https://www.eth3d.net/result_details?id=216)) ## Evaluation on Custom Dataset * For evaluation, we support preparing the custom dataset from COLMAP's results. The script `colmap_input.py` (modified based on the script from [MVSNet](https://github.com/YoYo000/MVSNet)) converts COLMAP's sparse reconstruction results into the same format as the datasets that we provide. After reconstruction, COLMAP will generate a folder `COLMAP/dense/`, which contains `COLMAP/dense/images/` and `COLMAP/dense/sparse`. Then you need to run like this: ``` python colmap_input.py --input_folder COLMAP/dense/ ``` * The default output location is the same as the input one. If you want to change that, set the `--output_folder` parameter. * The default behavior of the converter will find all possible related images for each source image. If you want to constrain the max number of related images set the `--num_src_images` parameter. * In `eval.sh`, set `CUSTOM_TESTING` as the root directory of the dataset, set `--output_folder` as the directory to store the reconstructed point clouds (default is same as input directory), set `--image_max_dim` to an appropriate size (this is determined by the available GPU memory and the desired processing speed) or use the native size by removing the parameter, and uncomment the evaluation command. Test on GPU by running `sh eval.sh`. ## Training Download pre-processed [DTU's training set](https://polybox.ethz.ch/index.php/s/ugDdJQIuZTk4S35). The dataset is already organized as follows: ``` root_directory ├── Cameras_1 │ ├── train │ │ ├── 00000000_cam.txt │ │ ├── 00000000_cam.txt │ │ └── ... │ └── pair.txt ├── Depths_raw │ ├── scan1 │ │ ├── depth_map_0000.pfm │ │ ├── depth_visual_0000.png │ │ ├── depth_map_0001.pfm │ │ ├── depth_visual_0001.png │ │ └── ... │ ├── scan2 │ └── ... └── Rectified ├── scan1_train │ ├── rect_001_0_r5000.png │ ├── rect_001_1_r5000.png │ ├── ... │ ├── rect_001_6_r5000.png │ ├── rect_002_0_r5000.png │ ├── rect_002_1_r5000.png │ ├── ... │ ├── rect_002_6_r5000.png │ └── ... ├── scan2_train └── ... ``` To use this dataset directly look into the [Legacy Training](#legacy-training) section below. For the current version of training the dataset needs to be converted to a format compatible with `MVSDataset` in `./datasets/mvs.py` using the script `convert_dtu_dataset.py` as follows: ``` python convert_dtu_dataset.py --input_folder <original_dataset> --output_folder <converted_dataset> --scan_list ./lists/dtu/all.txt ``` The converted dataset will now be in a f