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- W4385805005 abstract "Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform all current methods that are explicitly designed to exploit 3D information. This is counter-intuitive as even a simple inspection of the dataset shows that color-only methods are insufficient for images containing geometric anomalies. This motivates the question: how can anomaly detection methods effectively use 3D information? We investigate a range of shape representations including hand-crafted and deep-learning-based; we demonstrate that rotation invariance plays the leading role in the performance. We uncover a simple 3D-only method that beats all recent approaches while not using deep learning, external pre-training datasets, or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with color-based features, significantly outperforming previous state-of-the-art. Our method, dubbed BTF (Back to the Feature) achieves pixel-wise ROCAUC: 99.3% and PRO: 96.4% on MVTec 3D-AD." @default.
- W4385805005 created "2023-08-15" @default.
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- W4385805005 date "2023-06-01" @default.
- W4385805005 modified "2023-10-14" @default.
- W4385805005 title "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" @default.
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- W4385805005 doi "https://doi.org/10.1109/cvprw59228.2023.00298" @default.
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