HyperAI超神経

プラットフォーム

3D Anomaly Detection And Segmentation On

評価指標

Detection AUROC

Segmentation AUPRO

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

			Paper Title
Shape-Guided (only SDF)	0.916	0.931	Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
CPMF (2D+3D)	0.9515	0.9293	Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (FPFH)	0.782	0.924	Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
CPMF (3D)	0.8304	0.9230	Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
CPMF (2D)	0.8918	0.9145	Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
3D-ST_128	-	0.833	Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors
Voxel GAN	0.537	0.583	The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
Voxel VM	0.571	0.492	The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
Voxel AE	0.699	0.348	The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization

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3D Anomaly Detection And Segmentation On | SOTA | HyperAI超神経