GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment

Shang-Fu Chen; Jhih-Ciang Wu; Kuan-Chuan Peng; Wen-Huang Cheng; Kai-Lung Hua

cs.CVAug 27, 2026

GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment

Authors: Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng, Wen-Huang Cheng, Kai-Lung Hua

Organizations: National Taiwan University Taipei, Taiwan · National Taiwan Normal University Taipei, Taiwan · Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA · VinUniversity Hanoi, Vietnam · Microsoft Taiwan Corporation Taipei, Taiwan · National Taiwan University of Science and Technology Taipei, Taiwan

Abstract

Multi-view anomaly detection (MvAD) detects defects by exploiting complementary observations from multiple camera viewpoints. The central challenge is to fuse views with sufficient geometric awareness while remaining scalable to multi-class industrial settings. Existing methods typically fall into two extremes: voxel-based fusion provides explicit geometric alignment but requires costly 3D construction and class-specific assumptions, whereas lightweight patch-based fusion is efficient but relies on discrete candidate matching and lacks continuous cross-view correspondence. In this paper, we propose GeoMAD, a unified multi-view, multi-class AD framework that addresses both geometric correspondence deficiency and distributional inconsistency. Our Cross-view Deformable Fusion Module (CDFM) learns content-adaptive, view-pair-specific sampling offsets directly on 2D feature maps and arranges them across a multi-scale window pyramid with image-global reference sampling, enabling hierarchical cross-view correspondence without camera calibration, voxel construction, or class-specific 3D supervision. We further introduce Distributional View Alignment (DVA), a self-supervised cross-view regularization loss that aligns each view's bottleneck distribution against a per-instance view-centric target, enforcing global consistency without pixel-level correspondence. Together, CDFM and DVA bridge local geometric correspondence and global distributional consistency, providing geometry-aware and distribution-consistent fusion while preserving the efficiency of 2D feature-space learning. Extensive experiments on Real-IAD and MANTA-Tiny show that GeoMAD achieves strong detection and localization performance in unified MvAD.

Figures & tables

Figure 1: GeoMAD \xspace combines Cross-view Deformable Fusion Module (CDFM) and Distributional View Alignment (DVA) to achieve geometry-aware and distribution-consistent fusion directly in 2D feature space, addressing key limitations of existing methods [ 11 , 20 ] .

Method Properties	AD Categories
Method Properties	${\mathcal{G}}_{0}$	${\mathcal{G}}_{1}$	IDIF	MVAD	GeoMAD (ours) \xspace
Single model for all classes	✗	✓	✗	✓	✓
Designed for multi-view AD	✗	✗	✓	✓	✓
Learned 2D geometric correspondence	✗	✗	✗	✗	✓
Distributional view alignment	✗	✗	✗	✗	✓

Table 1: Characteristic comparison between GeoMAD (ours) \xspace and prior AD works.

{\mathcal{G}}_{0}

denotes class-specific single-view AD methods [ 17 , 12 , 24 , 5 , 6 , 32 , 4 ] ;

{\mathcal{G}}_{1}

denotes unified single-view AD methods [ 31 , 34 , 9 , 8 , 10 , 29 ] . IDIF [ 20 ] and MVAD [ 11 ] denote representative multi-view AD methods.

Figure 2: Overview of our proposed GeoMAD \xspace . Given multi-view input images, GeoMAD \xspace adopts a teacher-student architecture with the Cross-view Deformable Fusion Module (CDFM) for deformable cross-view feature fusion. Distributional View Alignment (DVA) mitigates distributional inconsistency by aligning bottleneck feature distributions across views. Multi-view anomaly scores are then aggregated for final detection.

Figure 3: The mechanism of CDFM. (a) CDFM predicts offsets $\boldsymbol{\Delta}_{q\to r}$ from the windowed query features and bilinearly samples reference keys and values at the deformed positions $\mathbf{p}+\boldsymbol{\Delta}_{q\to r}$ . Each query view produces $V{-}1$ offset fields, so windowed queries can deformably attend to features anywhere in each reference view, coupling cheap query windowing with global key/value reach. (b) Details of the offset network.

Method	S-AUROC	S-AP	S-F1	I-AUROC	I-AP	I-F1	P-AUROC	P-AP	P-F1	P-AUPRO
RD4AD ${}_{\text{CVPR'22}}$ [ 6 ]	78.3	63.7	68.3	80.5	59.5	62.4	91.8	29.1	34.7	78.8
UniAD ${}_{\text{NeurIPS'22}}$ [ 31 ]	74.9	59.7	65.2	82.7	64.3	65.1	90.7	26.1	32.4	79.6
DeSTSeg ${}_{\text{CVPR'23}}$ [ 37 ]	62.4	49.3	56.5	62.3	37.8	45.5	54.8	12.0	9.0	23.4
RealNet ${}_{\text{CVPR'24}}$ [ 36 ]	55.1	40.9	51.7	57.3	31.8	41.3	50.0	10.3	4.4	15.1
MVAD ${}_{\text{TMM'25}}$ [ 11 ]	85.0	76.5	73.8	85.2	69.9	67.9	93.7	32.7	38.5	80.7
GeoMAD (ours) \xspace	88.2	81.9	78.3	87.0	73.4	70.5	94.2	36.8	41.6	83.1

Table 2: Average performance comparison on MANTA-Tiny dataset. The best and second-best results are marked in bold and underlined .

Method	S-AUROC	S-AP	S-F1	I-AUROC	I-AP	I-F1	P-AUROC	P-AP	P-F1	P-AUPRO
RD4AD ${}_{\text{CVPR'22}}$ [ 6 ]	85.7	92.4	87.7	83.0	79.6	74.4	97.3	25.8	33.5	90.4
UniAD ${}_{\text{NeurIPS'22}}$ [ 31 ]	87.3	93.1	88.9	83.0	83.4	74.6	97.4	23.7	31.4	87.3
RD++ ${}_{\text{CVPR'23}}$ [ 27 ]	87.0	93.3	88.2	83.7	80.9	74.9	97.7	25.8	33.5	90.0
SimpleNet ${}_{\text{CVPR'23}}$ [ 17 ]	61.2	77.9	82.1	82.6	79.2	74.1	76.3	3.4	6.9	42.5
DeSTSeg ${}_{\text{CVPR'23}}$ [ 37 ]	89.0	94.6	89.2	82.3	79.2	73.2	94.6	37.9	41.7	40.6
RealNet ${}_{\text{CVPR'24}}$ [ 36 ]	72.0	87.1	82.5	65.2	65.4	62.7	60.5	27.2	24.9	28.7

Table 3: Average performance comparison on Real-IAD dataset. The best and second-best results are marked in bold and underlined .

Figure 4: Qualitative comparison on Real-IAD (left) and MANTA-Tiny (right). Each row shows one view of the same object instance ( $V=5$ views per sample). GeoMAD \xspace yields more spatially coherent localization with fewer false activations.

Dataset	CDFM	DVA	S-AUROC	S-AP	S-F1	I-AUROC	I-AP	I-F1	P-AUROC	P-AP	P-F1	P-AUPRO
MANTA-Tiny	✗	✗	78.3	63.7	68.3	80.5	59.5	62.4	91.8	29.1	34.7	78.9
	✓	✗	87.8	81.4	77.7	86.6	72.7	70.0	94.0	36.0	40.9	82.4
	✓	✓	88.2	81.9	78.3	87.0	73.4	70.5	94.2	36.8	41.6	83.1
Real-IAD	✗	✗	84.7	91.8	87.5	82.0	78.5	73.7	97.0	26.1	33.3	89.5
	✓	✗	91.6	95.8	91.0	87.3	85.5	77.9	97.8	35.7	41.0	92.0
	✓	✓	91.8	95.9	91.2	87.5	85.6	78.2	97.8	35.6	40.8	92.2

Table 4: Ablation study on CDFM and DVA. We evaluate the contribution of Cross-view Deformable Fusion Module (CDFM) and Distributional View Alignment (DVA) on MANTA-Tiny and Real-IAD, reporting sample-level (S), image-level (I), and pixel-level (P) metrics. Bold indicates the best result and underlined indicates the second best.

Kernel Size	S-AUROC	S-AP	S-F1	I-AUROC	I-AP	I-F1	P-AUROC	P-AP	P-F1	P-AUPRO
-	91.3	95.7	90.9	87.0	85.1	77.6	97.8	35.5	40.5	91.6
{3}	91.0	95.5	90.7	86.8	85.0	77.4	97.8	35.2	40.3	91.4
{5}	91.6	95.8	91.0	87.4	85.5	78.0	97.8	36.1	41.2	92.0
{7}	91.8	95.9	91.2	87.5	85.6	78.2	97.8	35.6	40.8	92.2
{9}	92.0	96.0	91.2	87.3	85.3	77.9	97.8	35.1	40.5	92.0
{5,7}	91.0	95.5	90.7	86.8	84.9	77.4	97.8	35.4	40.5	91.4

Table 5: Impact of smoothing kernel size in CDFM. We compare single-kernel sizes {3, 5, 7, 9} and a multi-kernel variant {5, 7} against a no-smoothing baseline (denoted “-”). The best and second-best results are marked in bold and underlined .

DVA Type	S-AUROC	S-AP	S-F1	I-AUROC	I-AP	I-F1	P-AUROC	P-AP	P-F1	P-AUPRO
View Pair	91.6	95.8	91.2	86.9	84.8	77.4	97.8	34.3	39.8	91.7
View Centric	91.8	95.9	91.2	87.5	85.6	78.2	97.8	35.6	40.8	92.2

Table 6: Alignment target in DVA. Comparison between pairwise alignment (View Pair) and view-centric alignment toward a shared per-instance centroid (View Centric).

Views	S-AUROC	S-AP	S-F1	I-AUROC	I-AP	I-F1	P-AUROC	P-AP	P-F1	P-AUPRO
5 (default)	91.8	95.9	91.2	87.5	85.6	78.2	97.8	35.6	40.8	92.2
4	90.9	95.5	90.4	87.2	85.3	78.0	97.7	35.5	40.8	91.9
3	89.1	94.7	89.1	86.9	84.8	77.5	97.5	35.3	40.6	91.8
2	85.5	92.9	87.3	86.7	83.4	76.5	97.1	34.2	39.6	91.5

Table 7: Robustness to missing views on the Real-IAD dataset. We evaluate model performance when reducing the number of available views from the default 5 down to 2 at inference time, using the model trained with all 5 views.

Method	Parameters	FLOPs	Train Memory
RD4AD ${}_{\text{CVPR'22}}$ [ 6 ]	80.61M	142.0G	11,614M
UniAD ${}_{\text{NeurIPS'22}}$ [ 31 ]	24.52M	16.8G	7,324M
ViTAD ${}_{\text{CVIU'25}}$ [ 34 ]	38.6M	48.3G	4,842M
MambaAD ${}_{\text{NIPS'24}}$ [ 9 ]	25.7M	41.5G	28,994M
MVAD ${}_{\text{TMM'25}}$ [ 11 ]	18.4M	45.0G	6,744M
GeoMAD (ours) \xspace	19.0M	46.8G	8,310M

Table 8: Computational efficiency comparison. Parameters, FLOPs, and training memory of GeoMAD \xspace against representative SOTA methods.

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SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection

Letian Bai, Chengyu Tao, Juan Du

Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China · The Hong Kong University of Science and Technology, Hong Kong SAR, 999077, China

Anomaly Detection Multimodal Anomaly Detection Industrial Anomaly Detection Multi-View Consistency Cross-Modal Alignment

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Multi-view anomaly detection aims to identify surface defects on complex objects using observations captured from multiple viewpoints. However, existing unsupervised methods often suffer from feature inconsistency arising from viewpoint variations and modality discrepancies. To address these challenges, we propose a Semantic and Geometric Alignment Network (SGANet), a unified framework for multimodal multi-view anomaly detection that effectively combines semantic and geometric alignment to learn physically coherent feature representations across viewpoints and modalities. SGANet consists of three key components. The Selective Cross-view Feature Refinement Module (SCFRM) selectively aggregates informative patch features from adjacent views to enhance cross-view feature interaction. The Semantic-Structural Patch Alignment (SSPA) enforces semantic alignment across modalities while maintaining structural consistency under viewpoint transformations. The Multi-View Geometric Alignment (MVGA) further aligns geometrically corresponding patches across viewpoints. By jointly modeling feature interaction, semantic and structural consistency, and global geometric correspondence, SGANet effectively enhances anomaly detection performance in multimodal multi-view settings. Extensive experiments on the SiM3D and Eyecandies datasets demonstrate that SGANet achieves state-of-the-art performance in both anomaly detection and localization, validating its effectiveness in realistic industrial scenarios.

Abstract

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