PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection
Authors: Qiang Zhou
Weize Li
Lihan Jiang
Guoliang Wang
Guyue Zhou
Shanghang Zhang
Hao Zhao
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: Anomaly Detection
Neural Radiance Field
TL;DR: A Dataset and Benchmark for Pose-agnostic Anomaly Detection

Abstract: Object anomaly detection is an important problem in the field of machine vision and has seen remarkable progress recently. However, two significant challenges hinder its research and application. First, existing datasets lack comprehensive visual information from various pose angles. They usually have an unrealistic assumption that the anomaly-free training dataset is pose-aligned, and the testing samples have the same pose as the training data. However, in practice, anomaly may exist in any regions on a object, the training and query samples may have different poses, calling for the study on pose-agnostic anomaly detection. Second, the absence of a consensus on experimental protocols for pose-agnostic anomaly detection leads to unfair comparisons of different methods, hindering the research on pose-agnostic anomaly detection. To address these issues, we develop Multi-pose Anomaly Detection (MAD) dataset and Pose-agnostic Anomaly Detection (PAD) benchmark, which takes the first step to address the pose-agnostic anomaly detection problem. Specifically, we build MAD using 20 complex-shaped LEGO toys including 4K views with various poses, and high-quality and diverse 3D anomalies in both simulated and real environments. Additionally, we propose a novel method OmniposeAD, trained using MAD, specifically designed for pose-agnostic anomaly detection. Through comprehensive evaluations, we demonstrate the relevance of our dataset and method. Furthermore, we provide an open-source benchmark library, including dataset and baseline methods that cover 8 anomaly detection paradigms, to facilitate future research and application in this domain. Code, data, and models are publicly available at https://github.com/EricLee0224/PAD.

PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection
Qiang Zhou Weize Li Lihan Jiang? f Guoliang Wang
Guyue Zhou' Shanghang Zhang Hao Zhao
1 AIR, Tsinghua University Wuhan University "National Key Laboratory for Multimedia
Information Processing, School of Computer Science, Peking University (bamboosdu, (rreizee2224@gmaar com shanghang@pku, edu. cn zhachao@air tsinghua edu.
Abstract
Object anomaly detection an important problem in the field of machine vision and has seen remarkable progress recently. However, two significant challenges hinder its research and application. First, existing datasets lack comprehensive visual information from various pose angles. They usually have an unrealistic assumption that the anomaly- samples have the same pose as the training data. However, in practice, anomaly may exist in any regions on object, the training and query samples may have different poses, calling for the study on pose-agnostic anomaly detection. Second, the absence of consensus on experimental protocols for pose-agnostic anomaly de- tection leads to unfair comparisons of different methods, hindering the research on pose-agnostic anomaly detection. To address these issues, we develop Multi-pose Anomaly Detection (MAD) dataset and Pose-agnostic Anomaly Detection (PAD) benchmark, which takes the first step to address the vose-agnostic anomaly detec- tion problem. Specifically, including 4K views with various poses, and high-quality and diverse 3D anomalies in both simulated and real environments. Additionally, we propose novel method OmniposeAD, trained using MAD, specifically designed for pose-agnostic anomaly detection. Through comprehensive evaluations, demonstrate the relevance of our dataset and method. Furthermore, we provide an open-source benchmark library, including dataset and baseline methods that cover 8 anomaly detection paradigms, to facilitate future research and application in this domain. Code, data, and models publicly available at http://gghub..om///nneeee///////
training dataset is pose-aligned, and the testing
build MAD using 20 complex-shapee LEGO toys
1 Introduction
Unsupervised visual anomaly detection attempts to detect shape or texture anomalies using normal samples and representations from pre-trained model. Due to the increasing demand for quality control of manufactured products and the complicated shape of object and its texture information, object-centric anomaly detection has attracted growing interest. However, existing object anomaly detection datasets and methods are designed under the pose-aligned assumption, which limits the ability to detect potentially occluded anomalies from arbitrary pose views. By overcoming the inherent constraints of pose-aligned anomaly detection approaches, the pose-agnostic anomaly detection setting offers enhanced flexibility and applicability real-world scenarios where a object may exhibit anomalies from diverse perspectives.
Equal contribution.
Work done during internship AIR.
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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w Pose
Pose
Query Pose
Train
Test
M
M
M
M
(a)
(b)
Figure 1: (a) Existing assumption for Pose-aligned Anomaly Detection; (b) Our introduced Pose-
agnostic Anomaly Detection setting. M denotes pre-trained AD model.
To address the pose-agnostic anomaly detection problem, two key challenges need to be overcome. First, we currently lack datasets that simultaneously provide RGB images of object's multiple viewpoints and its pose labels. This limitation hinders the model's ability to learn the decision boundaries in high-level feature space of anomaly- freee object in terms of both shape and texture, which is critical for performing accurate aose-agnostic anomaly detection. Second, in addition to above mentioned dataset being needed, how to measure and compare the performance of pose- agnostic anomaly detection models remains issue. Existing benchmarks focus on pose-aligned anomaly detection, making it difficult to establish fair criterion for evaluating the effectiveness of pose-agnostic anomaly detection methods.
To this end, we developed the multi-pose anomaly detection (MAD) dataset that contributes to a comprehensive evaluation of pose-agnostic anomaly detection. Our dataset consists of 20 diverse LEGO toys, capturing more than 11,000 images from different viewpoints covering a wide range of poses. Furthermore, the dataset includes three types of anomalies that carefully selected reflect both the simulated and real-world environments. To explore our MAD dataset, we make in- analyses from two perspectives (shape and texture) with interesting observations. We conducted the analysis of the shape and texture complexity for different categories of LEGO toys in the dataset to gain insight into the performance differences of the baseline methods. By investigating the correlation between model performance and attribute complexity, we aim to assess whether the baseline methods are effective in capturing the full range of visual information from normal objects. Alongside the dataset, we propose OmniposeAD which introduces neural radiance field into the anomaly detection paradigm for the first time. It overcomes the difficulty of learning the decision boundaries of a normal object from both shape and texture, has the ability to detect anomalies in multiple views of an object, and is even able to detect anomalies poses that have never been seen in the training set. The main contributions are summarized as follows:
We introduced Pose-agnostic Anomaly Detection (PAD), challenging setting for object anomaly detection from diverse viewpoints/poses, breaking free from the constraints of stereotypical pose alignment, and taking step forward to pratical anomaly detection and localization tasks.
We developed the Multi-poss posee Anomaly Detection (MAD) dataset, the first attempt at evaluating the pose-agnostic anomaly detection. Our dataset comprises MAD-Sim and MAD-Real, containing 11 11,000+ high-resolution RGB images of multi-pose views from 20 diverse shapes and colors of LEGO toys, offering pixel-precise ground truth for 3 types of anomalies.
We conducted comprehensive benchmark of the PAD setting, utilizing the MAD dataset. Across 8 distinct paradigms, we meticulously selected 10 state-of-the-arr methods for evaluation. In a pioneering effort, we delving into the previously unexplored realm of correlation between object attributes and anomaly detection performance.
We proposed OmniposeAD, utilizing NeRF to encode anomaly-free objects from diverse view- points/poses and comparing reconstructed normal reference with query image for pose-agnostic anomaly detection and localization. It outperforms previous methods quantitatively and qualita- tively the MAD benchmark, which indicates the promises of PAD setting.

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Table 1: Comparison of MAD and existing object anomaly detection datasets. Abbreviated letters
refer to the details'.
Datasets
Year.
Type.
Represent.
Sample statistics
Object Attributes
#Cls.
#Normal
#Abnoraal
Color
Structure
Pose
GDXray MVTec
2015
Real
RGB-D
19,407
Grayscale
C
AD
2019
Real
RGB
15
4.096
1,258
Diverse
S
3D AD
2021
Real
RGB/PC
2,904
948
Diverse
S
4
2022
Real
RGB
5
2.347
993
Diverse
C
MPDD 20
2021
Real
RGB
1,064
282
Diverse
C
X
Byecandies 8
2022
Syn.
RGB/D/N
13,250
2.250
Diverse
S
Vis.A 665
2022
Real
RGB
12
9,621
1,200
Diverse
S&C
X
MAD (Ours)
2023
Sim+Real
RGB
20
5,231
4,902
Diverse
S&C
2 Related Work
Object Anomaly Detection Datasets. The progress of object anomaly detection in industrial vision is significantly impeded by the scarcity of datasets containing high-quality annotated anomaly samples and comprehensive view information about normal objects. MVTee has developed series of widely-used useed photo-realistic industrial anomaly detection datasets: The objects provided by the AD dataset are simple, as discerning anomalies can be achieved solely from single view. Although the 3D-AD[7] dataset offers more complex objects, lacks RGB information from full range of views, requiring the supplementation of hard-to-capture point cloud data to detect subtle structural anomalies. The LOCO AD dataset provides rich global structural and logical information but is not suitable for fine-grained anomaly detection on individual objects. (DDXray(25) provides grayscale maps obtained through X-ray scans for visual discrimination of structural defects but lacks normal samples and color/texture information. The MPDD [0) dataset offers multi-angle information about the objects but is limited in size and lacks standardized backgrounds in the photos. Recently, Eyecandies(8 8 has introduced substantial collection of synthetic candy views captured under various lighting conditions and provides multi sensory inputs. However, there remains a significant gap between synthesized data and the real or simulated data domain. Vis (i5) contains objects with complex structures, multiple instances at different locations in single view, different objects across 3 domains, and multiple anomaly classes, thus is closer to the real world than MvTec-AD(5). To address these issues and enable exploration of the pose-agnostic AD problem, we propose Multi-pose Anomaly Detection (MAI)) dataset. We present comprehensive comparison between MAD and other representative object anomaly detection datasets in Table [
Unsupervised Anomaly Detection for Visual Inspection. Existing methods can be categorized into feature embedding basedd and reconstruction based strategies, according to a recent survey (24).
Feature embedding based methods such as Teacher-Student Architecture 6 51 15 10 33), One-
and Memory 50 aim to learn low- diwdemensional representations of input data to capture underlying patterns and anomalies. Reconstruction-baal based methods exploiting Autoencoder (AF) ( 46). Generative Adversarial Networks (GANs) 33 52. 23), and Transformer 27 57 model aim to learn a representation that can effectively reconstruct normal samples. Deviations in reconstruction quality indicate the presence of anomalies. BTF[19] reveals that using only angularly limited RGB images to determine whether an object is defective or not can easily overlook some obscured structural anomalies. Therefore is necessary to develop methods for 3D anomaly detection using 3D information, such as point clouds, depth, etc. AST 35 employs RGB image with depth information enhance anomaly detection performance. M3DM 47 and CPMF encourage the fusion of features from different modalities in RGB and point cloud information, and combine the local geometric information of the 3D modal with the global semantic information of the pseudo 2D modal.
Class Classification 17
[66] 62. 22 54], Distribution-mar 36 B3] 34 18 59
Bank 32
13
21
2,
53
61 60 14
58
29
Neural Radiance Field. Neural Radiation Field (NeRF) 26
implements implicit representation of the scene, receiving hundreds of sampling points along each camera ray and outputting the predicted color and density. Because NeRF can handle complex illumination and occlusion situations, we can use it to capture the color and shape properties of objects in real scenes, which means that NeRF has
Synn. PC, D. N. C and S denotes Synthesis, Point Cloud, Depth, Normal Map. Complex, and Simple. respectively.
3

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the potential to be applied to anomaly detection tasks. It has also been used for numerous applications,
such as large-scale urban reconstruction [48 49 40]. human face or body reconstruction [25 and
robotic applications such as pose estimation and [41 64 63 Since NeRF allows for the continuous modeling of 3D scenes and the re-rendering high-quality photos, can create reference
images from any angle for anomaly detection in this work.
3 MAD: Multi-pose Anomaly Detection Dataset
Burrs
Stains
Missing
AIR DISCOVER
M A
Multi-post pose nomaly Detection Dataset
(a)
(b)
Figure (a) Our LEGO family in MAD dataset; (b) Examples of three defect types.
3.1
Data Selection
Aiming to support pose-agnostic anomaly detection, dataset selects 20 LEGO animals toys with diverse shape complexity and color contrasts as target objects shown in Figure ([a), a). These selected toys encompass wide range of shapes and sizes, ranging from cat and pig to larger animals like unicorn and elephanr, along with other interesting animal forms. Each animal toy is meticulously designed and constructed ensure variety of shapes and complexities. In selecting these objects, we also paid attention to color contrasts. Bright and vibrant colors are used in the construction of each animal toy, making them more visually striking and easily recognizable in the images. The choice of color contrasts aims to assist algorithms in accurately detecting and localizing anomalies in different environments and backgrounds.
3.2 Data Processing and Annotation
We used Blender in combination with Ldrew (LEGO parts library) to build 20 different types of 3D LEGO animal models and generate anomalous such as burrs, stains and missing parts shown as Figure bb. To achieve full pose angle rendering of the normal samples, we established the center of the animal as the origin and positioned the rendering camera around the sphere. By maintaining a fixed radius, different theta/phi coordinate in the spherical coordinate system. In addition, we created ten cameras equally spaced along the Z-axis, with the camera orientation set face the animal. In order to edit the real anomalies on the simulation model, we hired professional LEGO players to manually modify the 20 types of LEGO animal models according to the types of anomalies encountered using PhotoShop. For the hand-edited anomalies, synchronized ground truth labels were produced by a computer vision researcher. Notably, only subset of images showed the anomaly portion, which required manual selection process to determine the appropriate data for anomaly detection. Ultimately, approximately 150-300 anomaly images were generated for each animal. To generate training data with more normal instances, we used the same approach. For the real dataset, we collect parts from the actual production line, including abnormal parts characterized by real burrs and stains. addition, we manually generated the missing-piect anomaly by removing LEGO parts. Given the rarity of anomalies in the manufacturing process LEGO toys and the difficulty obtaining these parts, we deliberately reduced the sample size of the real-world version.
generated multiple cameras simultaneously, each positioned

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3.3 Data Statisties and Split
We present the statistical information of the dataset in [ During the data spliting process. we followed design strategy of existing anomaly detection datasets. MAD-Sim is dataset created programmatically using Blender, allowing for controlled sample quantities. We divided it into training and test sets with a sample ratio of approximately 2:1 to explore anomaly detection algorithm performance on multi-view objects. For MAD-Real, the data was captured in real-world scenes using a camera. Anomalies are infrequent in actual production, and we aimed to reflect this reality. MAD-Real has approximately one-fifth the total sample count of MAD-Sim and follows a similar division into training and test sets, maintaining sample ratio of about 4:1.
3.4 Object Attributes Quantification
Anomaly detection algorithms evolving rapidly, but its often difficult to intuitively determine which one is the best from performance metrics when trying to deploy an algorithm to a real scenario. To reduce testing costs, we explore novel aspect in anomaly detection tasks, which is the relationship between object attributes and anomaly detection performance. When we are given an object to be tested, expect determine which algorithm is likely be the best, simply by referring the correlation between quantitative metrics the object's attributes, i.e., object shape complexity and color contrast, and the performance of different algorithms. The quantitative details are shown in Fig
compelccmylexit
fexture-complexity
100
Gorilo
Unicarn
Malard
Turile
Whole
Bird
Owl
Sabertoath
Swan
Shwep
Pig
Zabka
Phoeni
Elephard
Parrol
Cal
Scoppion
Obesoees
Bear
Puppy
Figure 3: Object attributes quantification of 20 LEGO toys.
4 Pose-agnostic Anomaly Detection Framework
4.1 Problem Definition
Our Pose-agnostic Anomaly Detection (PAD) setting introduced for object anomaly detection and localization tasks is shown Figure and it can be formally stated follow: Given training
T= [t, { == in which {11, 12, ... itv) } are the anomaly-free samples from object's multi pose view and each sample consists of a RGB image Irgb w/ pose information ppose- In addition, T belongs to certain object Oj. Oj E O, where O denotes the set of all objects categories. During testing, given query (normal or abnormal) image Q from object 0, w/o pose information Opose- the pre-trained AD model should discriminate whether or not the query image Q anomalous and localize the pixel-wise anomaly region if the anomaly is detected.
4.2 OmniposeAD
The OmniposeAD illustrated in Figure 4 consists of anomaly-free neural radiance field, coarse-to-fin -fnen pose estimation module, and anomaly detection and localization module. The input of OmniposcAD is query image w/o pose. Initially, undergoes the coarse-to-fin pose estimation module to obtain the accurate camera view pose 0pose- Subsequently, Opese is utilized in the pre-trained neural radiance field for rendering the normal reference. Finally, the reference is compared to the Q to extract the anomaly information.
4.2.1 Anomaly-free reference view synthesis
To reconstruct anomaly-free references from various viewpoints, we employ unsupervised algorithm capable of synthesizing novel views. With anomaly-free training set T represent anomaly-frec
5

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Training w/ pose
Backpropagation
Anomaly-free
Database
Coarse Pose
MLP
Query Image
W/o pobe
Query Image
Feature
Descriptor
Matching
Fine Pose
Reference
Randering
Figure 4: OmniposeAD consists of three componets. 1) The Neural Radiance Field is trained using multi-pose views of anomaly-free objects capture normal information. 2) The query image proceeds through two-stage pose estimation process, from coarse to fine: a matching descriptor is generated match with the anomaly-free database samples to obtain coarse pose, then the pose refined using the iNeRF [5] to obtain the reference pose. 3) The pre-trained Neural Radiance Field renders the normal reference, which is compared with the query image for anomaly detection and localization.
objects implicitly using vanilla neural radiance field, following (26 which employs multi layer perceptron (MLP) network and minimizes the photometric loss.
4.2.2 Coarse-to-fine pose estimation
To generate normal reference using the pre-trained neural radiance field, the fine camera pose pose of the query image needs to be determined. For this purpose, we employ the coarse-to- pose estimation module that combines image retrieval techniques and (N5RF(55).
On a coarse scale Our objective is to roughly align the query image Q with an anomaly-free image with camera pose in the training database i.e., find function that satisfies the following criteria:
HxWx3 64
l (()
q E R4
First, we utilize the pre-trained EfficientNet-BB model
43 as the backbone for feature extraction.
Following the standard procedure (58), input the multi-scale feature map into the network obtain the descriptor q. Given an 3-channel RGB query image Ligh of dimension H x W, our model ő extract a compact 64-dimensional (64D) descriptor embedding cxx). Then the image retrieval task[9] [ 30 considers database of images ligh for each sample 4; in the training set T and given query image Q, calculate:
argmin|lo(() 6(1)
(2)
Finally, the top-1 most similar image Ligh is retrieved. We utilize the associated pose 6pose of the image Lig as the initial coarse pose 8pose for the next stage.
On fine scale We refine the estimated pose using iNeRF 55 This method enables to fully utilize the pre-trained NeRF model and estimate camera poses. iNeRF uses the ability from NeRF to take some estimated camera pose 0 E SE(3) in the coordinate frame of the NeRF model and render a corresponding image observation, and then use the same photometric loss function as NeRF but updates the fine pose 6pose instead of the weights of the MLP network.
4.2.3 Anomaly detection and localization
When the normal reference rendered using 0pose and query image are available, we use the frozen pre-trained CNN backbone for feature extraction. The feature from layerl to layer5 are resized

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to the same size as the query image. Then the multi-scale feature and the query image are concatenate together to form a feature map, q RCx RC*HXW HxW with the corresponding input pixel. The query image corresponds to fq E RCxH*W, while the rendered reference corresponds to f E RCCHHXW We first define the feature difference map, d(i, u), as shown in Eqn. 3
d(i, u) = fi f(i u), where denotes the channel index and, u is the spatial position index (height together with width for simplicity).
(3)
s(u) u2
Anomaly detection aims to determine if an image contains anomalous regions. We intuitively use the maximum value of the averagely pooled s(u) as the anomaly score of the whole image.
Anomaly localization aims to localize anomalous regions, producing an anomaly score map, s(u) in Eqn. 4. which assigns an anomaly score for each pixel, s(u) is calculated as the L2 norm of the feature difference vector, d(: u) in Eqn. 3
Table 2: Quantitative results of pixel/image-level anomaly detection performance on MAD.
Carggry
Phentorm X
STIVM
Foslare Exhheddize losed
Foertew 50
(Tioa
CTA
Curpuse
DRAFM T5
PAVAR
Recusatructins-een -acc DKTRGAN
UISAD
Oars
Guilla
$8.4055.
$3.84333
01,4/5.1
54.769.2
91
35.11-
77.2/5L9
92,14558
94.21-
93.45655
99.9936
194001-5
45 6
1647714
$6.76
Tarl
5100614
85.953.7
50255.0
27.21
45.3/
50:482.8
99.1966
white
500896.1
18.8641
16.13322
10225711
00.00
53 1358
432075
Hird
#5A0219
406824
918873
05.2766
71.20-
00339611
0187113
95.1
172.
Owl
66.3722
51,8727
00
54.7765
35,0740
51.00
785007
56.769.5
90.1A
90 0660
99
Sabenookt
50-475.6
19:306.1
56 170.3
3
18:0642
71.21
26.2966
54.3922
16:4099
$1.399.5
01.07144
96. 1609
$1.1749
9630
97 2/7
Pix
$3,58577
$4.2516
00.6555.
97.172.1
15,665.7
$2.31-
63.99444
9222525
93.01
9435445
97.5 97.08L
Zulla
$4.905
16.25377
$6.5549
$7.0521
43.5/
0565517
86.4546
94.4-
25.7545
9H 5
Phoents
45-471-
151/9677
15111144
57 1911
ST/W19
51
62-40533
16.01
91AN23
Dephari
70.0720
$4061.1
54.053.4
$3.200
$1,1223
$7.746.1
$5.53.4
5970
Ca
7
4328822
95.113.1
94.76533
25.0052
79.0613
54.09122
91.3-
97.2649
Sompers
1638821
9B26619
76.39514
9199725
00.2M1 4
71.20-
BK496-M
97A-
95 WALL
51,98995
$1.2915
02.5657.
$5.0
96,3990
73.3
39.773.
92.75512
$8.81
93/6677
Hear
99.85422
SBAMLY
15.09129
92.266
800778
09.30-
392276.1
Papwy 13,063
$7.155.
Mizs
7437763
1933993
16.0503
9009713
8933652
39.30
$8.39
39. 19.0221
97.8009
5 Experiments and Analysis
Our experiments aim to broadly evaluate the performance of anomaly detection algorithms in pose- agnostic setting, where different types of anomalies may appear in different object poses. We selected representative state-of-the-an methods from different paradigms for fair preliminary benchmarking using the MAD dataset we developed with the aim of exploring the applicability of existing methods in the PAD setting. In addition, we evaluate the performance of our proposed OmniposeAD, normal reference reconstruction results, and the effect of training poses/viewpoints richness on performance.
Benchmark Methods Selection. The selection criteria for benchmark methods include represen- tativeness, superior performance, and availability of source code. To comprehensively investigate the performance of anomaly detection algorithms in the pose-agnostic anomaly detection setting, we selected representative methods from paradigms, the taxonomy can be found in the sup- plementary. For the Feature sameeddinn-bbsee strategy, we selected Patchcore (32. STFPM(45), Fastflow [59] CFlow [8). CFA(21], and Cutpaste 22). .. For the Reconstruction- based strategy, we selected 16 FT) OCR-GAN28), and UniAD(57).. It is important to note that 3D anomaly detection methods utilizing multimodal information were not considered in order to ensure a fair comparison, as the MAD dataset only provides RGB information.
Implementation Details. For the implementation details, we trained the OmniposeAD using the MAD dataset, employing the same hyperparameters for both synthetic and real scenes. During the anomaly-free novel view synthesis phase, the process took approximately 5 hours with a batch size of 4096. The image resolution was set to 400x400 for the simulated dataset and 504x378 for the
7

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real dataset to ensure efficient processing. Additionally, the coarse-to- toareeto--fne pose estimation process involved a total of 300 optimization steps. We initialized the learning rate 0.01 and decayed it to 5.8e-05 at epoch 50 to facilitate smooth optimization process, utilizing batch size of 3072. The entire framework required approximately 10-15 hours to run on a single NVIDIA Tesla A100.
Following previous work, specifically choose the Area Under the Receiver Operating Caracteristic Curve (AUROC) as the primary metric for evaluating the performance of anomaly segmentation at the pixel-level and anomaly classification at the image-level. While there exist various evaluation metric for these tasks, AUROC stands out as the most widely used and suitable metric for conducting comprehensive benchmarking. The AUROC score be calculated as follows:
Evaluation Metric.
AUROC = = (RTr)dRen
(5)
Here, RTP and RFp represent the pixcl/image-level true positive rate and false positive rate, respec- tively.
Missing
Missing
Stain
Missing
Burr
Input
Fastflow
CFlow
Patchcore
CFA
FAVAE
Ours
GT
Figure 5: Qualitative results visualization of anomaly localization performance on MAD-Sim.
5.1 Pose-agnostic Anomaly Detection Benchmark on MAD Dataset
Our experiments provide fair comparison between the state-of-the-ar methods and OmniposeAD in the PAD setting, including pixel and image-level AUROC performance. Table 2 and Figure 5 clearly demonstrate that Omnipose achieves the state-of-the-ar performance on MAD dataset for pose-agnostic anomaly detection evaluation. Specifically, OmniposeAD outperforms Feature UniAD[57] by large margin, +7.0/+19,
Embedding- CFlow 18 and Reconstruction-basse and +8. ++8.7/+28.7. 7, respectively. Its noteworthy that additional data (i.e., using pre-trained model on ImageNet during the training phase) always brings advantage. This consistent superiority can be attributed to ImageNet's extensive repository of object representations spanning diverse poses. In a direct comparison with methods not utilizing additional training data, our approach showeases remarkable enhancement, as evidenced by substantial increment of +29.20 229.2/48.3. Both the Feature Embedding and Reconstruction based methods, namely DRAFM (60) and Cutpaste 22, employ the generation of aseudo-anomalle as a data augmentation technique. However, these approaches

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exhibit notable decline in performance within the PAD setting. Objects may present different representations different poses, leading to complex patterns of visual anomalies. It is difficult to effectively characterize the anomalies of objects under different viewpoints through simple data enhancement methods.
Our benchmarks demonstrate the considerable potential of both feature embedding- baasd and reconstruction-basee approaches the PAD setting. In particular, for the pixel-level anomaly localiza- tion task, the performance of STFPM 45 (89.3), CFlow 18 (90.8), CFA (89 8)) FA and (87 .1) shows a slightly fluctuating but largely respectable performance. However, with the exception of our proposed OmniposeAD. the performance of benchmark methods in image-level anomaly detection tasks not satisfactory. It is worth noting that UniAD(57), although capable of learning multi- category anomaly boundaries using single model, still struggles to achieve satisfac- tory image-level anomaly detection performance for multi-pose anomalies in the PAD setting. To address this gap, algorithms that are adept at capturing the normal attributes of multi-pose objects need to explored achieve reasonable image-level anomaly detection performance within the PAD setting.
5.2 Correlation of performance with object attributes
The results are shown in Figure where the performance of most methods is positively correlated with color contrast and negatively correlated with structure complexity, which is consistent with intuition. Notably, Cutpaste 22), representative approach that generates anomalies and reconstructs them through sulf-supe task, stands out being sensitive color contrast but surprisingly tolerant towards shape complexity. Furthermore, we demonstrate the robustness of our proposed OmniposeAD changes in object attributes.
221M
Imput
FAVAF
Input
FAVAR
Ours
and
difiorcat
traaa with siniier
Figure 6: Correlation between object attributes and anomaly detection performance.
Figure 7: Visualization of reference re- construction between Omnipose and FAVAE
5.3 Validation of reference reconstruction on OmniposeAD
Both the reconstruction-basse method and proposed OmniposeAD essentially reconstruct the normal reference. In the quantitative results of the anomaly localization task, the representative reconstruction-basse method FAVAE achieved 89.4, and further visualized the reconstructed references to explore the gap. Figure shows that FAVAE does not reconstruct references for unseen poses well (the quantitative results do not align with visual facts), hinting that the existing evaluation metrics are potentially unsatisfactory in the PAD setting. Our further discussion is provided in Sec.4 of the Supplementary.
5.4 Effect of Dense-to-Sparse training data on OmniposeAD performance
In this section, we utilize the MAD- Real dataset to evaluate the impact of the transition from dense to sparse viewpoint training data on the performance of the OmniposeAD method, reflecting the lack of available viewpoints for real-world applications. We control the sample size of the training set for the OmniposeAD model to 100, 70, and 50, respectively. shown Table Despite the sparse training data leads to slight decrease in performance, the less pronounced drop in pixel-

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Input
GT
Our
Inpa
GT
Our
Figure 8: Qualitative results visualization of OmniposeAD anomaly localization performance from multi-view on MAD-Real.
level performance suggests that OmniposeAD excels in capturing local consistency within objects. The significant decrease the image-level indicates that sparse data impacts OmniposeAD's ability to learn the holistic normal features of object from global perspective. We further showed the multi-view anomaly localization performance OmniposeAD the MAD-Real dataset in Figure
Table 3: Dense-view to sparse-view object anomaly detection results on MAD-Real.
Sample Size
100
70
Sample Size
100
50
Object Class
Pixel
Piacl
Pixel
Object Class
Image
Piacl
Pixel
Pixel
Gonilla
93.6
99.5
85.4
97.5
91.0
97.4
Pig
75.7
96.4
73.9
95.7
72.8
95.4
Unicorn
84.4
96.1
Zalika
88.2
96.8
72.8
Malland
87.3
96.3
80.3
94.4
74.6
95.1
Phoenix
86.0
99.4
84.6
99.2
82.6
98.8
Turtle
99.1
95.4
95.1
92.3
83.4
90.4
Elephnn
91.0
98.1
82.5
95.9
85.1
96.5
Whale
Bird
92.0
95.1
91.6
91.7
89.1
93.4
Cat
78.9
97.6
73.5
97.6
73.7
97.1
Owl
89.1
99.3
78.8
98.1
98.8
Scorpion
87.4
94.2
77.0
91.8
74.3
91.5
Sahertooth
Swan
80.1
98.0
77.0
97.3
71.3
96.7
Rear
97.8
99.3
92.0
98.0
87.8
97.9
Sheep
84.3
97.6
83.2
92.8
93.1
86.5
95.8
95.3
Discussion
Conclusion. In this work, we introduce pose-agnostic anomaly detection setting, and develop the MAD dataset, which is the first exploration evaluate pose-agnostic anomaly detection algorithms. In addition, we propose OmniposeAD to solve the problem of unsupervised anomaly detection in different poses of object, which avoids missing potential occluded anomaly regions. Limitation. For the MAD dataset, which includes only standard rigid objects and no naturally varying objects, the complexity and diversity industrial products made difficult to collect and quantify complete sample of diverse features. For OmniposeAD, it is difficult to generalize to novel classes to obtain appreciable performance; for all modules, chose the vanilla method in pursuit of robustness, which has the potential for faster training times, less training data, and more realistic high-frequeng details.
7 Acknowledgment
We would like to express our sincere appreciation to Shenzhen Qianzhi Technology Lo.,Lid for their support in providing Lego toys manufacturing and defect samples.
10

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14


Reviews:
[{"review_id": "rGlsTTLDDc", "reviewer": "Reviewer_pCfQ", "summary": "", "questions": "", "limitations": "The authors have acknowledged the limitations in the conclusion section.", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- The proposed pose-agnostic anomaly detection setting addresses a real and intriguing problem. Anomaly detection on the production line does not guarantee that objects will consistently appear with fixed poses in the camera view. Consequently, objects with defects in unobserved orientations are easily overlooked. This paper offers a promising approach to tackle this critical challenge.\n- The authors present a well-designed and high-quality simulated+real dataset, incorporating RGB and pose information, to establish a benchmark for the PAD setting. It is worth noting that the file structure of the MAD dataset aligns with MvTec-AD, enabling easy replication of the benchmarking process and fostering the development of new algorithms.\n- The PAD benchmark evaluates multiple algorithmic paradigms, facilitating reproducibility and providing fresh insights through the analysis of quantified object attributes.\n- The authors propose a novel NeRF-based object anomaly detection framework for the PAD setting, exhibiting exceptional performance.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "z4vVIPNPMp", "reviewer": "Reviewer_ryha", "summary": "", "questions": "", "limitations": "While PAD demonstrates generalization capabilities for unseen views/angles of objects, it may face challenges when generalizing to new object categories. However, PAD remains a robust and applicable solution for industrial quality control and related applications.", "rating": 8, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. The authors present a novel perspective on the object anomaly detection task through PAD, along with a well-designed and user-friendly dataset, MAD. This contribution brings new challenges to the field and bridges the gap between research and practical applications, surpassing existing 2D/3D anomaly detection settings.\n2. The benchmarking approach, considering different AD paradigms, provides valuable insights for the application community to select appropriate AD methods. The inclusion of object attribute quantification as part of the benchmark enhances the practicality and relevance of the evaluation.\n3. The utilization of NeRF as an anomaly detection method in PAD is a novel contribution. The authors demonstrate its effectiveness through promising performance results.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "DuTl2JRL9T", "reviewer": "Reviewer_9dF6", "summary": "", "questions": "", "limitations": "yes but not enough", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. the quality of the newly introduced dataset is high in terms of outlier type, data size, etc. More important, it is a unique dataset about pose-specific anomalies.\n\n2. comprehensive experiments with 10 baselines ( most of which were within recent three years) can serve as a benchmark.\n\n3. the proposed PAAD outperformed all baselines\n\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "x4QPVC02T6", "reviewer": "Reviewer_upSJ", "summary": "", "questions": "", "limitations": "While the authors mention a limitation of their proposed baseline method, limitations of the dataset (cf. my comments in \"Opportunities For Improvement\") are not sufficiently discussed in the paper.", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "The problem setting of pose-agnostic anomaly detection is a valuable new addition to the field of anomaly detection. While the single-pose setting of other visual anomaly detection datasets is appropriate for certain applications (e.g., in very controlled industrial settings), the pose-agnostic setting will be more appropriate for some other applications.\nThe variety introduced by different poses can make the problem more challenging.\n\nThe benchmark conducted by the authors shows that many recently introduced visual anomaly detection algorithms are not very suited to the pose-agnostic setting, indicating the need for new approaches. The introduced NeRF-based method is a valuable first step in that direction.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: The paper received four reviews. The authors submitted rebuttals and the reviewers engaged in conversation with the authors, who clarified the raised weaknesses. The reviewers appreciate the well-designed and high-quality simulated+real dataset, which addresses a real and intriguing problem. The reviewers also consider that the utilization of NeRF as an anomaly detection method is a novel contribution. In summary, the reasons to accept the paper clearly outweigh the negative points. However, the authors are asked to include the relevant points mentioned in the discussion to the camera ready.
Decision: Accept (Poster)