IAENet fuses 2D and 3D anomaly scores with a learned importance-aware weighting and a margin-based selector loss, achieving state-of-the-art point-level localization on MVTec 3D-AD.
Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detection
IAENet fuses 2D and 3D anomaly scores with a learned importance-aware weighting and a margin-based selector loss, achieving state-of-the-art point-level localization on MVTec 3D-AD.