HyperAI초신경

홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

한국어

HyperAI초신경

3D Anomaly Detection And Segmentation On

평가 지표

Detection AUROC

Segmentation AUPRO

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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

0 of 9 row(s) selected.