HyperAI超神经

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HyperAI超神经

3D Anomaly Detection And Segmentation On

评估指标

Detection AUROC

Segmentation AUPRO

评测结果

各个模型在此基准测试上的表现结果

模型名称	Detection AUROC	Segmentation AUPRO	Paper Title	Repository
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
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
Shape-Guided (only SDF)	0.916	0.931	Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
CPMF (2D)	0.8918	0.9145	Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
Voxel AE	0.699	0.348	The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
3D-ST_128	-	0.833	Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors	-
CPMF (2D+3D)	0.9515	0.9293	Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
CPMF (3D)	0.8304	0.9230	Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection

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