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Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications.
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On adversarial robustness of 3d point cloud classification under adaptive attacks
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Shapenet: An information-rich 3d model repository
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Voxnet: A 3d convolutional neural network for real-time object recognition
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A review of point cloud registration algorithms for mobile robotics
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Voting for voting in online point cloud object detection
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3d shapenets: A deep representation for volumetric shapes
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He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Point cloud noise and outlier removal for image-based 3d reconstruction
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
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Attention is all you need
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Autoaugment: Learning augmentation policies from data
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Large-scale point cloud semantic segmentation with superpoint graphs
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Tof lidar development in autonomous vehicle
Liu, J., Sun, Q., Fan, Z., and Jia, Y · 2018
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High-performance virtual reality volume rendering of original optical coherence tomography point-cloud data enhanced with real-time ray casting
Maloca, P. M., de Carvalho, J. E. R., Heeren, T., Hasler, P. W., Mushtaq, F., Mon-Williams, M., Scholl, H. P., Balaskas, K., Egan, C., Tufail, A., et al · 2018
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Mining point cloud local structures by kernel correlation and graph pooling
Shen, Y., Feng, C., Yang, Y., and Tian, D · 2018
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Open3D: A modern library for 3D data processing
Zhou, Q.-Y., Park, J., and Koltun, V · 2018
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3d point cloud analysis for automatic inspection of aeronautical mechanical assemblies
Adallah, H. B., Orteu, J.-J., Dolives, B., and Jovančević, I · 2019
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3d point cloud compression: A survey
Cao, C., Preda, M., and Zaharia, T · 2019
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On evaluating adversarial robustness
Carlini, N., Athalye, A., Papernot, N., Brendel, W., Rauber, J., Tsipras, D., Goodfellow, I., Madry, A., and Kurakin, A · 2019
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Robustbench: a standardized adversarial robustness benchmark
Croce, F., Andriushchenko, M., Sehwag, V., Debenedetti, E., Flammarion, N., Chiang, M., Mittal, P., and Hein, M · 2020
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Self-robust 3d point recognition via gather-vector guidance
Dong, X., Chen, D., Zhou, H., Hua, G., Zhang, W., and Yu, N · 2020
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Improving robustness against common corruptions by covariate shift adaptation
Schneider, S., Rusak, E., Eck, L., Bringmann, O., Brendel, W., and Bethge, M · 2020
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Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
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Tent: Fully test-time adaptation by entropy minimization
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Hendrycks, D. and Dietterich, T · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B · 2019
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Extending adversarial attacks and defenses to deep 3d point cloud classifiers
Liu, D., Yu, R., and Su, H · 2019
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Benchmarking robustness in object detection: Autonomous driving when winter is coming
Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A. S., Bethge, M., and Brendel, W · 2019
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Pytorch: An imperative style, high-performance deep learning library
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Pointrcnn: 3d object proposal generation and detection from point cloud
Shi, S., Wang, X., and Li, H · 2019
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Kpconv: Flexible and deformable convolution for point clouds
Thomas, H., Qi, C. R., Deschaud, J.-E., Marcotegui, B., Goulette, F., and Guibas, L. J · 2019
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On isometry robustness of deep 3d point cloud models under adversarial attacks
Zhao, Y., Wu, Y., Chen, C., and Lim, A · 2020
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Are transformers more robust than cnns?
Bai, Y., Mei, J., Yuille, A., and Xie, C · 2021
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Defending against image corruptions through adversarial augmentations
Calian, D. A., Stimberg, F., Wiles, O., Rebuffi, S.-A., Gyorgy, A., Mann, T., and Gowal, S · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Revisiting point cloud shape classification with a simple and effective baseline
Goyal, A., Law, H., Liu, B., Newell, A., and Deng, J · 2021
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Pct: Point cloud transformer
Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R. R., and Hu, S.-M · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al · 2021
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Regularization strategy for point cloud via rigidly mixed sample
Lee, D., Lee, J., Lee, J., Lee, H., Lee, M., Woo, S., and Lee, S · 2021
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Pointguard: Provably robust 3d point cloud classification
Liu, H., Jia, J., and Gong, N. Z · 2021
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On interaction between augmentations and corruptions in natural corruption robustness
Mintun, E., Kirillov, A., and Xie, S · 2021
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Lidar with velocity: Motion distortion correction of point clouds from oscillating scanning lidars
Yang, W., Gong, Z., Huang, B., and Hong, X · 2021
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Center-based 3d object detection and tracking
Yin, T., Zhou, X., and Krahenbuhl, P · 2021
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Yu, J., Zhang, C., Wang, H., Zhang, D., Song, Y., Xiang, T., Liu, D., and Cai, W · 2021
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Pointcutmix: Regularization strategy for point cloud classification
Zhang, J., Chen, L., Ouyang, B., Liu, B., Zhu, J., Chen, Y., Meng, Y., and Wu, D · 2021
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Point transformer
Zhao, H., Jiang, L., Jia, J., Torr, P. H., and Koltun, V · 2021
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3d shape generation and completion through point-voxel diffusion
Zhou, L., Du, Y., and Wu, J · 2021
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