3D Anomaly Detection and Segmentation
8 papers with code • 2 benchmarks • 2 datasets
3D-Only Anomaly Detection and Segmentation
Benchmarks
These leaderboards are used to track progress in 3D Anomaly Detection and Segmentation
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MVTEC 3D-AD
DrivAerNet
Most implemented papers
The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
We introduce the first comprehensive 3D dataset for the task of unsupervised anomaly detection and localization.
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
caoyunkang/CPMF
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The 3D and 2D modality features are aggregated to obtain the CPMF for PCD anomaly detection.
Multi-time-horizon Solar Forecasting Using Recurrent Neural Network
sakshi-mishra/solar-forecasting-RNN
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The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon.
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity.
Anomaly Detection Requires Better Representations
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry.
Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
jayliu0313/Shape-Guided
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ICML 2023
We present a shape-guided expert-learning framework to tackle the problem of unsupervised 3D anomaly detection.
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection
hustCYQ/GLFM-Multi-class-3DAD
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journal 2025
Specifically, GLFM is structured into three stages: Stage-I proposes an anomaly synthesis pipeline that stretches point clouds to create abundant anomaly data that are utilized to adapt the point cloud feature extractor for better feature representation.
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection
Next, we present the Corrective Force Prediction Network (CFP-Net), which uses complementary representations for point-level analysis to simulate the different contributions from internal and external corrective forces.
