RobustPointSet
A benchmark dataset to facilitate augmentation-independent robustness analysis of point cloud classification models. RobustPointSet comes with 6 different transformations: Noise, Translation, Missing part, Sparse, Rotation, and Occlusion.
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 labels_test.npy
.
Benchmarks
Table 1: Training-domain validation results on our RobustPointSet test sets. The Noise column, for example, shows the result of training on the Original train set and testing with the Noise test set. RotInv refers to rotation-invariant models.
Type | Method | Original | Noise | Translation | Missing part | Sparse | Rotation | Occlusion | Average |
---|---|---|---|---|---|---|---|---|---|
General | PointNet | 89.06 | 74.72 | 79.66 | 81.52 | 60.53 | 8.83 | 39.47 | 61.97 |
General | PointNet++ (MSG) | 91.27 | 5.73 | 91.31 | 53.69 | 6.65 | 13.02 | 64.18 | 46.55 |
General | PointNet++ (SSG) | 91.47 | 14.90 | 91.07 | 50.24 | 8.85 | 12.70 | 70.23 | 48.49 |
General | DGCNN | 92.52 | 57.56 | 91.99 | 85.40 | 9.34 | 13.43 | 78.72 | 61.28 |
General | PointMask | 88.53 | 73.14 | 78.20 | 81.48 | 58.23 | 8.02 | 39.18 | 60.97 |
General | DensePoint | 90.96 | 53.28 | 90.72 | 84.49 | 15.52 | 12.76 | 67.67 | 59.40 |
General | PointCNN | 87.66 | 45.55 | 82.85 | 77.60 | 4.01 | 11.50 | 59.50 | 52.67 |
General | PointConv | 91.15 | 20.71 | 90.99 | 84.09 | 8.65 | 12.38 | 45.83 | 50.54 |
General | Relation-Shape-CNN | 91.77 | 48.06 | 91.29 | 85.98 | 23.18 | 11.51 | 75.61 | 61.06 |
RotInv | SPHnet | 79.18 | 7.22 | 79.18 | 4.22 | 1.26 | 79.18 | 34.33 | 40.65 |
RotInv | PRIN | 73.66 | 30.19 | 41.21 | 44.17 | 4.17 | 68.56 | 31.56 | 41.93 |
Table 2: Leave-one-out validation strategy classification results on our RobustPointSet test sets. For example, the Noise column shows the result of training on {Original, Translation, Missing part, Sparse, Rotation,Occlusion} train sets and testing with the Noise test set. RotInv refers to rotation-invariant models.
Type | Method | Original | Noise | Translation | Missing part | Sparse | Rotation | Occlusion | Average |
---|---|---|---|---|---|---|---|---|---|
General | PointNet | 88.35 | 72.61 | 81.53 | 82.87 | 69.28 | 9.42 | 35.96 | 62.86 |
General | PointNet++ (MSG) | 91.55 | 50.92 | 91.43 | 77.16 | 16.19 | 12.26 | 70.39 | 58.56 |
General | PointNet++ (SSG) | 91.76 | 49.33 | 91.10 | 78.36 | 16.72 | 11.27 | 68.33 | 58.12 |
General | DGCNN | 92.38 | 66.95 | 91.17 | 85.40 | 6.49 | 14.03 | 68.79 | 60.74 |
General | PointMask | 88.03 | 73.95 | 80.80 | 82.83 | 63.64 | 8.97 | 36.69 | 62.13 |
General | DensePoint | 91.00 | 42.38 | 90.64 | 85.70 | 20.66 | 8.55 | 47.89 | 55.26 |
General | PointCNN | 88.91 | 73.10 | 87.46 | 82.06 | 7.18 | 13.95 | 52.66 | 57.90 |
General | PointConv | 91.07 | 66.19 | 91.51 | 84.01 | 19.63 | 11.62 | 44.07 | 58.30 |
General | Relation-Shape-CNN | 90.52 | 36.95 | 91.33 | 85.82 | 24.59 | 8.23 | 60.09 | 56.79 |
RotInv | SPHnet | 79.30 | 8.24 | 76.02 | 17.94 | 6.33 | 78.86 | 35.96 | 43.23 |
RotInv | PRIN | 76.54 | 55.35 | 56.36 | 59.20 | 4.05 | 73.30 | 36.91 | 51.67 |
Publication
Please cite the paper below if you use RobustPointSet in your research.
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
@article{taghanaki2020robustpointset,
title={RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers},
author={Saeid Asgari Taghanaki and Jieliang Luo and Ran Zhang and Ye Wang and Pradeep Kumar Jayaraman and Krishna Murthy Jatavallabhula},
year={2020},
journal={arXiv preprint arXiv:2011.11572}
}
Download
The dateset consists of two parts: Part I and Part II. Please download both parts and unzip Part I, which will automatically extract the two parts into the same folder.
License
Please refer to the dataset license.