Fetching the paper…
Reading the bibliography…
Despite significant advances in image anomaly detection and segmentation, few methods use 3D information.
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24
1981
Earlier work this paper cites.
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD (1996)
1996
Earlier work this paper cites.
Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern recognition 30
1997
Earlier work this paper cites.
Scholkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C.: Support vector method for novelty detection. In: NIPS (2000)
2000
Earlier work this paper cites.
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection. In: Applications of data mining in computer security, pp. 77–101. Springer (2002)
2002
Earlier work this paper cites.
Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. SIGGRAPH (2003)
2003
Earlier work this paper cites.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International journal of computer vision 60
2004
Earlier work this paper cites.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). vol. 1, pp. 886–893. Ieee (2005)
2005
Earlier work this paper cites.
Latecki, L.J., Lazarevic, A., Pokrajac, D.: Outlier detection with kernel density functions. In: International Workshop on Machine Learning and Data Mining in Pattern Recognition. pp. 61–75. Springer (2007)
2007
Earlier work this paper cites.
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining. pp. 413–422. IEEE (2008)
2008
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (fpfh) for 3d registration. In: 2009 IEEE International Conference on Robotics and Automation. pp. 3212–3217 (2009). https://doi.org/10.1109/ROBOT.2009.5152473
2009
Earlier work this paper cites.
Jolliffe, I.: Principal component analysis. Springer (2011)
2011
Earlier work this paper cites.
Glodek, M., Schels, M., Schwenker, F.: Ensemble gaussian mixture models for probability density estimation. Computational Statistics 28
2013
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1912–1920 (2015)
2015
Earlier work this paper cites.
Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3d semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
2016
Cited alongside, same era.
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
2016
Cited alongside, same era.
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International Conference on Information Processing in Medical Imaging (2017)
2017
Cited alongside, same era.
Zeng, A., Song, S., Nießner, M., Fisher, M., Xiao, J., Funkhouser, T.: 3dmatch: Learning local geometric descriptors from rgb-d reconstructions. In: CVPR (2017)
2017
Cited alongside, same era.
2020
Later among the works it cites.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9729–9738 (2020)
2020
Later among the works it cites.
Simarro Viana, J., de la Rosa, E., Vande Vyvere, T., Robben, D., Sima, D.M., et al.: Unsupervised 3d brain anomaly detection. In: International MICCAI Brainlesion Workshop. pp. 133–142. Springer (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: NeurIPS (2018)
2018
Cited alongside, same era.
Ruff, L., Gornitz, N., Deecke, L., Siddiqui, S.A., Vandermeulen, R., Binder, A., Müller, E., Kloft, M.: Deep one-class classification. In: ICML (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D., Chen, H.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. ICLR (2018)
2018
Cited alongside, same era.
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9592–9600 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Hendrycks, D., Mazeika, M., Kadavath, S., Song, D.: Using self-supervised learning can improve model robustness and uncertainty. In: NeurIPS (2019)
2019
Cited alongside, same era.
Ao, S., Hu, Q., Yang, B., Markham, A., Guo, Y.: Spinnet: Learning a general surface descriptor for 3d point cloud registration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021)
2021
Later among the works it cites.
Bengs, M., Behrendt, F., Krüger, J., Opfer, R., Schlaefer, A.: Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain mri. International journal of computer assisted radiology and surgery 16
2021
Later among the works it cites.
2021
Later among the works it cites.
Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: International Conference on Pattern Recognition. pp. 475–489. Springer (2021)
2021
Later among the works it cites.
Li, C.L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: Self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9664–9674 (2021)
2021
Later among the works it cites.
Reiss, T., Cohen, N., Bergman, L., Hoshen, Y.: Panda: Adapting pretrained features for anomaly detection and segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2806–2814 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Closest in time.
2022
Closest in time.