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We present a new method for the unsupervised detection of geometric anomalies in high-resolution 3D point clouds.
Parametric correspondence and chamfer matching: Two new techniques for image matching
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Partial differential equations
L. C. Evans · 2010
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Unique signatures of histograms for local surface description
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Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite
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ImageNet Classification with Deep Convolutional Neural Networks
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A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
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SHOT: Unique signatures of histograms for surface and texture description
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3D ShapeNets: A Deep Representation for Volumetric Shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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Defect Detection in SEM Images of Nanofibrous Materials
D. Carrera, F. Manganini, G. Boracchi, and E. Lanzarone · 2016
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Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
W. Kehl, F. Milletari, F. Tombari, S. Ilic, and N. Navab · 2016
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Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features
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Introducing MVTec ITODD — A Dataset for 3D Object Recognition in Industry
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3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser · 2017
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Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
C. Baur, B. Wiestler, S. Albarqouni, and N. Navab · 2019
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SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall · 2019
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Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
P. Bergmann, S. Löwe, M. Fauser, D. Sattlegger, and C. Steger · 2019
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Fishyscapes: A Benchmark for Safe Semantic Segmentation in Autonomous Driving
H. Blum, P.-E. Sarlin, J. Nieto, R. Siegwart, and C. Cadena · 2019
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Where’s Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection
P. Burlina, N. Joshi, and I.-J. Wang · 2019
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Fully convolutional geometric features
C. Choy, J. Park, and V. Koltun · 2019
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Image Anomalies: A Review and Synthesis of Detection Methods
Attention guided anomaly localization in images
S. Venkataramanan, K.-C. Peng, R. V. Singh, and A. Mahalanobis · 2020
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PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
S. Xie, J. Gu, D. Guo, C. Qi, L. Guibas, and O. Litany · 2020
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Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI
M. Bengs, F. Behrendt, J. Krüger, R. Opfer, and A. Schlaefer · 2021
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The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
P. Bergmann, K. Batzner, M. Fauser, D. Sattlegger, and C. Steger · 2021
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Combining GANs and AutoEncoders for efficient anomaly detection
F. Carrara, G. Amato, L. Brombin, F. Falchi, and C. Gennaro · 2021
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T. Ehret, A. Davy, J.-M. Morel, and M. Delbracio · 2019
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A Benchmark for Anomaly Segmentation
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
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Sub-image anomaly detection with deep pyramid correspondences
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Latent feature decentralization loss for one-class anomaly detection
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T. Defard, A. Setkov, A. Loesch, and R. Audigier · 2021
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Learning semantic segmentation of large-scale point clouds with random sampling
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Deep learning for anomaly detection: A review
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Panda: Adapting pretrained features for anomaly detection and segmentation
T. Reiss, N. Cohen, L. Bergman, and Y. Hoshen · 2021
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Transfer Learning Gaussian Anomaly Detection by Fine-Tuning Representations
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Multiresolution knowledge distillation for anomaly detection
M. Salehi, N. Sadjadi, S. Baselizadeh, M. H. Rohban, and H. R. Rabiee · 2021
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Unsupervised 3D Brain Anomaly Detection
J. Simarro Viana, E. de la Rosa, T. Vande Vyvere, D. Robben, D. M. Sima, and CENTER-TBI Participants and Investigators · 2021
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The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
P. Bergmann, X. Jin, D. Sattlegger, and C. Steger · 2022
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CFLOW-AD: Real-Time Unsupervised Anomaly Detection With Localization via Conditional Normalizing Flows
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Image anomaly detection using normal data only by latent space resampling
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