# A 3D Multimodal Feature for Infrastructure Anomaly Detection

arXiv: https://arxiv.org/abs/2502.05779 · HTML: https://mlsanity.com/paper/2502.05779/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 64 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2006.16067/) (2020)
- [PPFNet: Global Context Aware Local Features for Robust 3D Point Matching](https://mlsanity.com/paper/1802.02669/) (2018)
- [The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization](https://mlsanity.com/paper/2112.09045/) (2021)
- [Multimodal Industrial Anomaly Detection via Hybrid Fusion](https://mlsanity.com/paper/2303.00601/) (2023)
- [Real3D-AD: A Dataset of Point Cloud Anomaly Detection](https://mlsanity.com/paper/2309.13226/) (2023)
- [Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection](https://mlsanity.com/paper/2303.13194/) (2023)
- [Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping](https://mlsanity.com/paper/2312.04521/) (2023)
- [Robust Anomaly Detection in Images using Adversarial Autoencoders](https://mlsanity.com/paper/1901.06355/) (2019)
- [Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture](https://mlsanity.com/paper/2007.00477/) (2020)
- [Deep Learning for Detecting Building Defects Using Convolutional Neural Networks](https://mlsanity.com/paper/1908.04392/) (2019)

## Built on by (most cited first, 1 of 1)

- [Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection](https://mlsanity.com/paper/2505.05901/) (2025)

## Abstract (arXiv)

Ageing structures require periodic inspections to identify structural defects. Previous work has used geometric distortions to locate cracks in synthetic masonry bridge point clouds but has struggled to detect small cracks. To address this limitation, this study proposes a novel 3D multimodal feature, 3DMulti-FPFHI, that combines a customized Fast Point Feature Histogram (FPFH) with an intensity feature. This feature is integrated into the PatchCore anomaly detection algorithm and evaluated through statistical and parametric analyses. The method is further evaluated using point clouds of a real masonry arch bridge and a full-scale experimental model of a concrete tunnel. Results show that the 3D intensity feature enhances inspection quality by improving crack detection; it also enables the identification of water ingress which introduces intensity anomalies. The 3DMulti-FPFHI outperforms FPFH and a state-of-the-art multimodal anomaly detection method. The potential of the method to address diverse infrastructure anomaly detection scenarios is highlighted by the minimal requirements for data compared to learning-based methods. The code and related point cloud dataset are available at https://github.com/Jingyixiong/3D-Multi-FPFHI.