Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection-Reference-Cited by-同舟云学术

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Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Author:

Horwitz Eliahu¹,Hoshen Yedid¹

Affiliation:

1. The Hebrew University of Jerusalem,School of Computer Science and Engineering,Israel

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10208270/10208119/10208423.pdf?arnumber=10208423

Reference44 articles.

1. PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization

2. Fast Point Feature Histograms (FPFH) for 3D registration

3. Histograms of Oriented Gradients for Human Detection

4. Deep one-class classification;ruff;ICML,2018

5. A Geometric Framework for Unsupervised Anomaly Detection

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DAUP: Enhancing point cloud homogeneity for 3D industrial anomaly detection via density-aware point cloud upsampling;Advanced Engineering Informatics;2024-10

2. Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection;Applied Sciences;2024-08-09

3. 3D Industrial anomaly detection via dual reconstruction network;Applied Intelligence;2024-07-30

4. Rotation invariance and equivariance in 3D deep learning: a survey;Artificial Intelligence Review;2024-06-07

5. Incremental Template Neighborhood Matching for 3D anomaly detection;Neurocomputing;2024-05

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