fix header text

update requirements, add PPSurf as copy

lfs track ply

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 *.obj filter=lfs diff=lfs merge=lfs -text
+*.ply filter=lfs diff=lfs merge=lfs -text

README.md

@@ -4,6 +4,7 @@ emoji: 🗿
 colorFrom: gray
 colorTo: blue
 sdk: gradio
+sdk_version: 4.44.1
 pinned: true
 license: mit
 suggested_hardware: a10g-small

app.py

@@ -91,8 +91,7 @@ def main():
 
     subprocess.run(['pip', 'list'])
 
-    description_header = '# PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction\n
-    Note: Hugginface disabled docker support for Zero-GPU without notification and no solution. I will fix this space when I find the time.'
+    description_header = '# PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction'
 
     description_col0 = '''## [Github](https://github.com/cg-tuwien/ppsurf)
     Supported input file formats: 

ppsurf/.gitignore

@@ -0,0 +1,145 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+.idea/
+
+*.zip
+*.pth
+
+results/
+models/
+logs/
+datasets/*
+!datasets/abc_minimal/
+logs_framework/
+debug/
+.vscode/
+backups/
+lightning_logs/
+pl_models/

ppsurf/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Philipp Erler
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ppsurf/README.md

@@ -0,0 +1,323 @@
+# PPSurf
+Combining Patches and Point Convolutions for Detailed Surface Reconstruction
+
+This is our implementation of [PPSurf](https://www.cg.tuwien.ac.at/research/publications/2024/erler_2024_ppsurf/),
+a network that estimates a signed distance function from point clouds. This SDF is turned into a mesh with Marching Cubes.
+
+![PPSurf teaser](images/teaser.png)
+
+This is our follow-up work for [Points2Surf](https://www.cg.tuwien.ac.at/research/publications/2020/erler-p2s/).
+It uses parts of [POCO](https://github.com/valeoai/POCO), mainly the network and mesh extraction.
+This work was published in [Computer Graphics Forum (Jan 2024)](https://onlinelibrary.wiley.com/doi/10.1111/cgf.15000).
+
+
+## Setup
+
+We tested this repository on these systems:
+* Windows 10/11 and Ubuntu 22.04 LTS
+* CUDA 11.7, 11.8 and 12.1
+
+To manage the Python environments, we recommend using [Micromamba](https://github.com/mamba-org/mamba), 
+a much faster Anaconda alternative. 
+To install it, [follow this guide](https://mamba.readthedocs.io/en/latest/micromamba-installation.html#umamba-install). 
+
+Alternatively, you can install the required packages with conda by simply replacing the 'mamba' calls with 'conda'.
+Finally, you can use Pip with the requirements.txt.
+
+``` bash
+# clone this repo, a minimal dataset is included
+git clone https://github.com/ErlerPhilipp/ppsurf.git
+
+# go into the cloned dir
+cd ppsurf
+
+# create the environment with the required packages
+mamba env create --file pps{_win}.yml
+
+# activate the new environment
+mamba activate pps
+```
+Use `pps_win.yml` for Windows and `pps.yml` for other OS.
+
+Test the setup with the minimal dataset included in the repo:
+``` bash
+python full_run_pps_mini.py
+```
+
+## Datasets, Model and Results
+
+Datesets:
+``` bash
+# download the ABC training and validation set
+python datasets/download_abc_training.py
+
+# download the test datasets
+python datasets/download_testsets.py
+```
+
+Model:
+``` bash
+python models/download_ppsurf_50nn.py
+```
+Let us know in case you need the other models from the ablation. 
+They were trained using old, unclean code and are not directly compatible with this repo. 
+
+Results:
+
+Download the results used for the paper from [here](https://www.cg.tuwien.ac.at/research/publications/2024/erler_2024_ppsurf/).
+This includes meshes and metrics for the 50NN variant.
+
+## Reconstruct single Point Clouds
+
+After the setup, you can reconstruct a point cloud with this simple command:
+``` bash
+python pps.py rec {in_file} {out_dir} {extra params}
+
+# example
+python pps.py rec "datasets/abc_minimal/04_pts_vis/00010009_d97409455fa543b3a224250f_trimesh_000.xyz.ply" "results/my clouds/" --model.init_args.gen_resolution_global 129
+```
+Where *in_file* is the path to the point cloud and *out_dir* is the path to the output directory.  
+This will download our pre-trained 50NN model if necessary and reconstruct the point cloud with it.    
+You can append additional parameters as described in [Command Line Interface Section](#Command-Line-Interface).  
+
+*rec* is actually not a sub-command but is converted to *predict* with the default parameters before parsing. 
+You can use the *predict* sub-command directly for more control over the reconstruction:
+``` bash
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt --trainer.logger False --trainer.devices 1 \
+  --data.init_args.in_file {in_file} --model.init_args.results_dir {out_dir}
+```
+
+Using the *predict* sub-command will **not** download our pre-trained model. You can download it manually:
+``` bash
+python models/download_ppsurf_50nn.py
+```
+
+Supported file formats are:
+- PLY, STL, OBJ and other mesh files loaded by [trimesh](https://github.com/mikedh/trimesh).  
+- XYZ as whitespace-separated text file, read by [NumPy](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html). 
+Load first 3 columns as XYZ coordinates. All other columns will be ignored.
+- NPY and NPZ, read by [NumPy](https://numpy.org/doc/stable/reference/generated/numpy.load.html).
+NPZ assumes default key='arr_0'. All columns after the first 3 columns will be ignored.
+- LAS and LAZ (version 1.0-1.4), COPC and CRS loaded by [Laspy](https://github.com/laspy/laspy). 
+You may want to sub-sample large point clouds to ~250k points to avoid speed and memory issues.
+For detailed reconstruction, you'll need to extract parts of large point clouds.
+
+
+## Replicate Results
+
+Train, reconstruct and evaluate to replicate the main results (PPSurf 50NN) from the paper
+``` bash
+python full_run_pps.py
+```
+
+Training takes about 5 hours on 4 A40 GPUs. By default, training will use all available GPUs and CPUs.
+Reconstructing one object takes about 1 minute on a single A40. The test sets have almost 1000 objects in total. 
+
+Logging during training with Tensorboard is enabled by default. 
+We log the loss, accuracy, recall and F1 score for the sign prediction. 
+You can start a Tensorboard server with: 
+``` bash
+tensorboard --logdir models
+```
+
+
+## Command Line Interface
+
+PPSurf uses the Pytorch-Lightning [CLI](https://lightning.ai/docs/pytorch/stable/cli/lightning_cli.html).
+The basic structure is:
+``` bash
+# CLI command template 
+python {CLI entry point} {sub-command} {configs} {extra params}
+```
+Where the *CLI entry point* is either `pps.py` or `poco.py` and *sub-command* can be one of *[fit, test, predict]*.  
+*Fit* trains a model, *test* evaluates it and *predict* reconstructs a whole dataset or a single point cloud.
+
+*Configs* can be any number of YAML files. Later ones override values from earlier ones. 
+This example adapts the default POCO parameters to PPSurf needs and uses all GPUs of our training server:
+``` bash
+-c configs/poco.yaml -c configs/ppsurf.yaml -c configs/device_server.yaml
+```
+
+You can override any available parameter explicitly:
+``` bash
+--model.init_args.gen_resolution_global 129 --debug True
+```
+
+When running *test*, *predict* or *rec*, you need to consider a few more things.
+Make sure to specify the model checkpoint! 
+Also, you need to specify a dataset, since the default is the training set.
+Finally, you should disable the logger, or it will create empty folders and logs. 
+``` bash
+--ckpt_path 'models/{name}/version_{version}/checkpoints/last.ckpt' --data.init_args.in_file 'datasets/abc_minimal/testset.txt' --trainer.logger False
+```
+where *name* is e.g. ppsurf and *version* is usually 0. 
+If you run the training multiple times, you need to increment the version number.
+
+Appending this will print the assembled config without running anything:
+``` bash
+--print_config
+```
+
+These are the commands called by full_run_pps.py to reproduce our results *PPSurf 50 NN*:
+``` bash
+# train
+python pps.py fit -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/device_server.yaml -c configs/ppsurf_50nn.yaml
+
+# test
+python pps.py test -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/abc/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+
+# predict all ABC datasets
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/abc/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/abc_extra_noisy/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/abc_noisefree/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+
+# predict all Famous datasets
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/famous_original/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/famous_noisefree/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/famous_sparse/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/famous_dense/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/famous_extra_noisy/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+
+# predict all Thingi10k datasets
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/thingi10k_scans_original/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/thingi10k_scans_noisefree/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/thingi10k_scans_sparse/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/thingi10k_scans_dense/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/thingi10k_scans_extra_noisy/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+  
+# predict the real-world dataset
+python pps.py predict -c configs/poco.yaml -c configs/ppsurf.yaml -c configs/ppsurf_50nn.yaml \
+  --data.init_args.in_file datasets/real_world/testset.txt --ckpt_path models/ppsurf_50nn/version_0/checkpoints/last.ckpt \
+  --trainer.logger False --trainer.devices 1
+  
+ # create comparison tables (will have only 50NN column)
+ python source/figures/comp_all.py
+```
+
+
+## Outputs and Evaluation
+
+**Training**:
+Model checkpoints, hyperparameters and logs are stored in `models/{model}/version_{version}/`. 
+The version number is incremented with each training run.
+The checkpoint for further use is `models/{model}/version_{version}/checkpoints/last.ckpt`.
+
+**Testing**:
+Test results are stored in `results/{model}/{dataset}/metrics_{model}.xlsx`.
+This is like the validation but on all data of the test/val set with additional metrics.
+
+**Reconstruction**:
+Reconstructed meshes are stored in `results/{model}/{dataset}/meshes`. 
+After reconstruction, metrics are computed and stored in `results/{model}/{dataset}/{metric}_{model}.xlsx`,
+where *metric* is one of *[chamfer_distance, f1, iou, normal_error]*.
+
+**Metrics**:
+You can (re-)run the metrics, e.g. for other methods, with:
+``` bash
+python source/make_evaluation.py
+```
+You may need to adjust *model_names* and *dataset_names* in this script. 
+This supports the results of other methods if they are in the same structure as ours.
+
+**Comparisons**:
+We provide scripts to generate comparisons in `source/figures`:
+``` bash
+python source/figures/comp_{comp_name}.py
+```
+This will:
+- assemble the per-shape metrics spreadsheets of all relevant methods in `results/comp/{dataset}/{metric}.xlsx`.
+- compute and visualize the Chamfer distance, encoded as vertex colors in 
+  `results/comp/{dataset}/{method}/mesh_cd_vis` as PLY.
+- render the reconstructed mesh with and without distance colors in `results/comp/{dataset}/{method}/mesh_rend` and
+  `results/comp/{dataset}/{method}/cd_vis_rend` as PNG.
+- render the GT mesh in `results/comp/{dataset}/mesh_gt_rend` as PNG. Note that this does only work if a real screen is attached.
+- assemble the per-method mean, median and stddev for all metrics in `results/comp/{comp_name}.xlsx`.
+- assemble all renderings as a qualitative report in `results/comp/{comp_name}.html`.
+- assemble per-dataset mean for all relevant datasets, methods and metrics in `results/comp/reports/{comp_name}` 
+  as spreadsheet and LaTex table.
+
+**Figures**:
+You can prepare Chamfer distance data and render the results with Blender using these scripts:
+``` bash
+python source/figures/prepare_figures.py
+python source/figures/render_meshes_blender.py
+```
+This requires some manual camera adjustment in Blender for some objects.
+Please don't ask for support on this messy last-minute code.
+
+
+## Trouble Shooting
+
+On Windows, you might run into DLL load issues. If so, try re-installing intel-openmp:
+``` bash
+mamba install -c defaults intel-openmp --force-reinstall
+```
+
+Conda/Mamba might run into a compile error while installing the environment. If so, try updating conda:
+``` bash
+conda update -n base -c defaults conda
+```
+
+Pip might fail when creating the environment. If so, try installing the Pip packages from the `pps.yml` manually.
+
+On Windows, Pip install may raise a 
+"Microsoft Visual C++ 14.0 or greater is required. 
+Get it with "Microsoft C++ Build Tools" error. 
+In this case, install the MS Visual Studio build tools, 
+as described on [Stackoverflow](https://stackoverflow.com/questions/64261546/how-to-solve-error-microsoft-visual-c-14-0-or-greater-is-required-when-inst).
+
+
+## Updates
+
+### 2023-10-13
+
+Improved speed by using [pykdtree](https://github.com/storpipfugl/pykdtree) 
+instead of [Scipy KDTree](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html) for k-NN queries
+
+
+## Citation
+If you use our work, please cite our paper:
+```
+@article{ppsurf2024,
+author = {Erler, Philipp and Fuentes-Perez, Lizeth and Hermosilla, Pedro and Guerrero, Paul and Pajarola, Renato and Wimmer, Michael},
+title = {PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction},
+journal = {Computer Graphics Forum},
+volume = {n/a},
+number = {n/a},
+pages = {e15000},
+keywords = {modeling, surface reconstruction},
+doi = {https://doi.org/10.1111/cgf.15000},
+url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.15000},
+eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.15000},
+abstract = {Abstract 3D surface reconstruction from point clouds is a key step in areas such as content creation, archaeology, digital cultural heritage and engineering. Current approaches either try to optimize a non-data-driven surface representation to fit the points, or learn a data-driven prior over the distribution of commonly occurring surfaces and how they correlate with potentially noisy point clouds. Data-driven methods enable robust handling of noise and typically either focus on a global or a local prior, which trade-off between robustness to noise on the global end and surface detail preservation on the local end. We propose PPSurf as a method that combines a global prior based on point convolutions and a local prior based on processing local point cloud patches. We show that this approach is robust to noise while recovering surface details more accurately than the current state-of-the-art. Our source code, pre-trained model and dataset are available at https://github.com/cg-tuwien/ppsurf.}
+}
+```

ppsurf/configs/device_server.yaml

@@ -0,0 +1,13 @@
+trainer:
+  strategy: ddp
+  # strategy: ddp_find_unused_parameters_true
+
+model:
+  init_args:
+    workers: &num_workers 48
+
+data:
+  init_args:
+    use_ddp: True
+    workers: *num_workers
+    batch_size: 12  # 50 / 4 = 12.5

ppsurf/configs/poco.yaml

@@ -0,0 +1,77 @@
+debug: False
+seed_everything: 42
+
+trainer:
+  max_epochs: 150
+  default_root_dir: 'models/poco'
+  strategy: auto
+  accelerator: gpu
+  devices: -1
+  precision: 16-mixed
+  num_sanity_val_steps: 0
+  log_every_n_steps: 1
+  logger:
+    class_path: pytorch_lightning.loggers.TensorBoardLogger
+    init_args:
+      save_dir: 'models'
+  callbacks:
+  - class_path: source.cli.PPSProgressBar
+  - class_path: LearningRateMonitor
+    init_args:
+      logging_interval: step
+  - class_path: ModelCheckpoint
+    init_args:
+      save_last: True
+      save_top_k: 0
+
+data:
+  class_path: source.poco_data_loader.PocoDataModule
+  init_args:
+    use_ddp: False
+    in_file: datasets/abc_train/testset.txt
+    padding_factor: 0.05
+    seed: 42
+    manifold_points: 10000
+    patches_per_shape: -1
+    do_data_augmentation: True
+    batch_size: 10
+    workers: 8
+
+model:
+  class_path: source.poco_model.PocoModel
+  init_args:
+    output_names:
+      - 'imp_surf_sign'
+    in_channels: 3
+    out_channels: 2
+    k: 64
+    network_latent_size: 32
+    gen_subsample_manifold_iter: 10
+    gen_subsample_manifold: 10000
+    gen_resolution_global: 257
+    rec_batch_size: 50000
+    gen_refine_iter: 10
+    workers: 8
+    lambda_l1: 0.0
+    results_dir: 'results'
+    name: 'poco'
+    debug: False
+
+optimizer:
+  class_path: torch.optim.AdamW
+  init_args:
+    lr: 0.001
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1e-5
+    weight_decay: 1e-2
+    amsgrad: False
+
+lr_scheduler:
+  class_path: torch.optim.lr_scheduler.MultiStepLR
+  init_args:
+    milestones:
+    - 75
+    - 125
+    gamma: 0.1

ppsurf/configs/poco_mini.yaml

@@ -0,0 +1,13 @@
+trainer:
+  default_root_dir: 'models/poco_mini'
+
+model:
+  init_args:
+    name: 'poco_mini'
+    gen_resolution_global: 129  # half resolution
+    rec_batch_size: 25000  # half memory
+
+data:
+  init_args:
+    in_file: datasets/abc_minimal/testset.txt  # small dataset
+    batch_size: 10  # 16 GB GPU memory

ppsurf/configs/ppsurf.yaml

@@ -0,0 +1,13 @@
+trainer:
+  default_root_dir: 'models/ppsurf'
+
+model:
+  class_path: source.ppsurf_model.PPSurfModel
+  init_args:
+    network_latent_size: 256
+    num_pts_local: 50
+    pointnet_latent_size: 256
+    debug: False
+    
+data:
+  class_path: source.ppsurf_data_loader.PPSurfDataModule

ppsurf/configs/ppsurf_100nn.yaml

@@ -0,0 +1,8 @@
+trainer:
+  default_root_dir: 'models/ppsurf_100nn'
+
+model:
+  init_args:
+    name: 'ppsurf_100nn'
+    num_pts_local: 100
+

ppsurf/configs/ppsurf_10nn.yaml

@@ -0,0 +1,8 @@
+trainer:
+  default_root_dir: 'models/ppsurf_10nn'
+
+model:
+  init_args:
+    name: 'ppsurf_10nn'
+    num_pts_local: 10
+

ppsurf/configs/ppsurf_200nn.yaml

@@ -0,0 +1,9 @@
+trainer:
+  default_root_dir: 'models/ppsurf_200nn'
+
+model:
+  init_args:
+    name: 'ppsurf_200nn'
+    num_pts_local: 200
+    rec_batch_size: 25000
+

ppsurf/configs/ppsurf_25nn.yaml

@@ -0,0 +1,8 @@
+trainer:
+  default_root_dir: 'models/ppsurf_25nn'
+
+model:
+  init_args:
+    name: 'ppsurf_25nn'
+    num_pts_local: 25
+

ppsurf/configs/ppsurf_50nn.yaml

@@ -0,0 +1,8 @@
+trainer:
+  default_root_dir: 'models/ppsurf_50nn'
+
+model:
+  init_args:
+    name: 'ppsurf_50nn'
+    num_pts_local: 50
+

ppsurf/configs/ppsurf_mini.yaml

@@ -0,0 +1,13 @@
+trainer:
+  default_root_dir: 'models/ppsurf_mini'
+
+model:
+  init_args:
+    name: 'ppsurf_mini'
+    gen_resolution_global: 129  # half resolution
+    rec_batch_size: 25000  # half memory
+
+data:
+  init_args:
+    in_file: datasets/abc_minimal/testset.txt  # small dataset
+    batch_size: 10  # 16 GB GPU memory

ppsurf/configs/profiler.yaml

@@ -0,0 +1,8 @@
+trainer:
+  profiler:
+    class_path: source.cli.PPSProfiler
+    init_args:
+      export_to_chrome: False
+      emit_nvtx: False
+      with_stack: False
+

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