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README.md

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-# RobustPointSet
+# RobustPointSet
+A benchmark dataset to facilitate augmentation-independent robustness analysis of point cloud classification models. RobustPointSet comes with 6 different transformation: Noise, Translation, Missing part, Sparse, Rotation, and Occlusion. 
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+![alt text](https://github.com/AutodeskAILab/RobustPointSet/blob/main/RobustPointSet.png?raw=true)
+
+--------------
+
+### Evaluation Strategies
+
+We test two different evaluation strategies on more than 10 models:
+
+#### Strategy 1 (training-domain validation)
+For this strategy, we train on `train_original.npy` without applying any data-augmentation, and test on each test set (i.e. `test_*.npy` ) separately.
+
+#### Strategy 2 (leave-one-out validation)
+For this strategy, each time we concatenate 6 train sets (i.e. the `train_*.npy` ones), and test on the test set (i.e. `test_*.npy` ) of the taken-out group. We repeat this process for all the groups. For example, we train with concatenation of `{train_original.npy, train_noise.npy, train_missing_part.npy, train_occlusion.npy, train_rotation.npy, train_sparse.npy}` and test on `test_translate.npy`. Similar to strategy 1, we don't apply any data-augmentation here. For both the strategies, the same label files can be used i.e. `labels_train.npy` and `lables_test.npy`.
+
+-----------------
+
+
+This dataset is provided for the convenience of academic research only, and is provided without any representations or warranties, including warranties of non-infringement or fitness for a particular purpose. Please cite the following paper if you use the dataset in your researcha
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