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-# NAISR
-NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
+# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
+Pytorch implementation for our `NAISR` paper
+[NAISR: A 3D Neural Additive Model for Interpretable Shape Representation](https://arxiv.org/abs/2303.09234), ICLR 2024 **Spotlight**.
+Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer.
+UNC-Chapel Hill
-Codes on the way...
+#### [Paper](https://arxiv.org/abs/2303.09234) | [Project Page](https://uncbiag.github.io/NAISR/) | [Colab Demos](https://colab.research.google.com/drive/1OudGynEydIXpAgfA9lvi5d0L86kK-uwg?usp=sharing)
+
+
+
+
+Please cite as:
+```
+@inproceedings{
+jiao2024naisr,
+title={\texttt{NAISR}: A 3D Neural Additive Model for Interpretable Shape Representation},
+author={Yining Jiao and Carlton Jude ZDANSKI and Julia S Kimbell and Andrew Prince and Cameron P Worden and Samuel Kirse and Christopher Rutter and Benjamin Shields and William Alexander Dunn and Jisan Mahmud and Marc Niethammer},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=wg8NPfeMF9}
+}
+```
+
+>[!NOTE]
+>For the three datasets used in `NAISR` paper, we provided this [Colab demo](https://colab.research.google.com/drive/1OudGynEydIXpAgfA9lvi5d0L86kK-uwg) to investigate the shapes learned with `NAISR`.
+We also provide the instructions on this page to apply `NAISR` to your customized shape analysis questions.
+
+
+## Installation
+The code is tested with ``python=3.9``, ``torch=2.1.0``, ``torchvision=0.15.2``.
+```
+git clone https://github.com/uncbiag/NAISR
+cd NAISR
+```
+Now, create a new conda environment and install required packages accordingly.
+```
+conda create -n naisr python=3.9
+conda activate naisr
+pip install -r requirements.txt
+```
+
+
+## Data and Model Weights
+We train and test our method on three datasets: Starman, ADNI Hippocampus, and Pediatric Airways.
+
+| Dataset | Description | Dataset Link | Model Link |
+|-----------|----------------------------------------------|:------------------------------------:|:------------------------------------:|
+|Starman | simulated 2D Starman shapes | [simulation code)][StarmanData] | [weights][StarmanModel] |
+|ADNI Hippocampus | 1632 hippocampus from ADNI | [official site][ADNIData] | [weights][ADNIModel] |
+|Pedeatric Airway | 357 pediatric airway shapes | NA | [weights][AirwayModel] |
+
+
+
+[ADNIData]: https://ida.loni.usc.edu/login.jsp?project=ADNI
+[StarmanData]:
+
+[ADNIModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_adni.pth
+[StarmanModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_starman.pth
+[AirwayModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_pediatric_airway.pth
+
+
+## Visualization of Existing Shape Space
+To get the shape space like the following figures for the three datasets used in the paper, one just need to apply the model weights with the following command,
+
+First, download the `NAISR model weights`. These weights will be automatically saved to the ``checkpoints`` folder.
+```
+python download.py
+```
+
+Run interactive GUI with the downloaded weights. The ``assets`` contains images for demo.
+```
+python evolution_shapematrix.py -examples/hippocampus/naigsr_0920_base.json
+```
+
+
+
+
+## Customize
+### Data Preprocessing
+
+#### Alignment with Rigid Transformation
+The shapes to explore need to be registered with a rigid transformation (translation + rotation).
+If paired point clouds are available, we recommend to use ; otherwise, we recommend to use ICP to register the point clouds.
+In our case, we use airway landmarks to learn the rigid transformation; and ICP algorithms to register the point clouds of the hippocampi.
+
+#### SDF Extraction
+
+###
+```json
+ {
+ "Description" : [ "This experiment learns a shape representation for starman dataset." ],
+ "Device":0,
+ "DataSource": {"train": "/home/jyn/NAISR/examples/starman/2dshape_train_with_temp.csv",
+ "test": "/home/jyn/NAISR/examples/starman/2dshape_test_with_temp.csv"},
+ "Split": null,
+ "Network": "DeepNAIGSR",
+ "NumEpochs": 300,
+ "LoggingRoot": "/playpen-raid/jyn/NAISR/log",
+ "ExperimentName": "DeepNAIGSR_STARMAN3D_0222_256_base",
+
+ "EpochsTilCkpt": 10,
+ "StepsTilSummary": 1000,
+ "UseLBFGS": false,
+ "DoublePrecision": false,
+ "CheckpointPath": "",
+ "CodeLength": 256,
+
+ "AdditionalSnapshots" : [ 50, 100, 200, 300, 400, 500 ],
+ "LearningRateSchedule" : [
+ {
+ "Type": "Step",
+ "Initial": 0.00005,
+ "Interval": 1000,
+ "Factor": 0.5
+ },
+ {
+ "Type": "Step",
+ "Initial": 0.001,
+ "Interval": 1000,
+ "Factor": 0.5
+ }],
+ "SamplesPerScene" : 750,
+ "BatchSize": 64,
+ "DataLoaderThreads": 4,
+ "ClampingDistance": 1,
+
+ "Articulation": true,
+ "NumAtcParts": 1,
+ "TrainWithParts": false,
+ "Class": "starman",
+ "Attributes": ["cov_1", "cov_2"],
+ "TemplateAttributes": {"cov_1": 0, "cov_2": 0},
+ "Backbone": "siren",
+ "PosEnc": false,
+ "InFeatures": 2,
+ "HiddenFeatures": 256,
+ "HidenLayers": 6,
+ "OutFeatures": 1,
+ "Loss": {
+ "whether_sdf": true,
+ "whether_normal_constraint": true,
+ "whether_inter_constraint": true,
+ "whether_eikonal": true,
+ "whether_code_regularization": true}
+```
+### Training
+
+### Testing
+
+### Application - Visualization of Your Shape Space
+
+
+
+
+Instructions on the way...
+
+
+
+
+
+## Cite this work
+```
+@inproceedings{
+jiao2024naisr,
+title={\texttt{NAISR}: A 3D Neural Additive Model for Interpretable Shape Representation},
+author={Yining Jiao and Carlton Jude ZDANSKI and Julia S Kimbell and Andrew Prince and Cameron P Worden and Samuel Kirse and Christopher Rutter and Benjamin Shields and William Alexander Dunn and Jisan Mahmud and Marc Niethammer},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=wg8NPfeMF9}
+}
+```
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content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
+
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
@@ -104,6 +105,16 @@
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
+Pytorch implementation for our `NAISR` paper