Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection
Authors: Yuyang Yu
Zhengwei Chen
Xuemiao Xu
Lei Zhang
Haoxin Yang
Yongwei Nie
Shengfeng He
Venue: NeurIPS 2025 poster ()
Keywords: Point Cloud
Anomaly Detection
Primary area: applications

Abstract: 3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability. However, current memory bank-based methods often suffer from inconsistent feature transformations and limited discriminative capacity, particularly in capturing local geometric details and achieving rotation invariance. These limitations become more pronounced when registration fails, leading to unreliable detection results. We argue that point-cloud registration plays an essential role not only in aligning geometric structures but also in guiding feature extraction toward rotation-invariant and locally discriminative representations. To this end, we propose a registration-induced, rotation-invariant feature extraction framework that integrates the objectives of point-cloud registration and memory-based anomaly detection. Our key insight is that both tasks rely on modeling local geometric structures and leveraging feature similarity across samples. By embedding feature extraction into the registration learning process, our framework jointly optimizes alignment and representation learning. This integration enables the network to acquire features that are both robust to rotations and highly effective for anomaly detection. Extensive experiments on the Anomaly-ShapeNet and Real3D-AD datasets demonstrate that our method consistently outperforms existing approaches in effectiveness and generalizability.

Registration is a Powerful Rotation-Invariacee Learner for 3D Anomaly Detection
Yuyang Yu!-
Zhengwei Chen
Xuemiao Xu
Lei Zhangat
Haoxin Yang'
Yongwei Nie!
Shengfeng He
'ooth China University of Technology *Guangdong University of Petrochemical Technology
"Singapore Management University
vyyoung0611@gmal {xuemx, nieyongvei) edu. cn harxis @outlook.ooooonmm
chen_ www_130063...
zhanglei @gdupt. edu.cn cn shengfenghe@amu. edu. sg
zhanglei@gdupt. https: / //t//gittt thub.
Abstract
3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability. However, current memory bank-based methods often suffer from inconsistent feature transformations and limited discriminative capacity, particularly in capturing local geometric details and achieving rotation invariance. These limitations become more pronounced when registration fails, leading to unreliable detection results. We argue that point-cloud registration plays an essential role not only aligning geometric struc- tures but also in guiding feature extraction toward rotation-invarian and locally discriminative representations. To this end, we propose a eggistrationiinuceed rotation-invarian feature extraction framework that integrates the objectives of point-cloud registration and memory-based anomaly detection. Our key insight is that both tasks rely modeling local geometric structures and leveraging feature similarity across samples. By embedding feature extraction into the registration learning process, framework jointly optimizes alignment and representation learning. This integration enables the network to acquire features that are both ro- bust to rotations and highly effective for anomaly detection. Extensive experiments on the Anomaly ShaapeNet and Real3D-AD datasets demonstrate that our method consistently outperforms existing approaches in effectiveness and generalizability.
Introduction
3D anomaly detection aims to identify structural defects in point-cloud data, with critical applications in industrial quality control. Despite the emergence of domain-specifie datasets such Real3D- AD [I] and Anomaly-ShapeNet (2), the rarity of real-world anomalies (e.g., orientation shift and other irregularities (3551) presents persistent challenge. Consequently, most existing approaches adopt unsupervised paradigms that rely solely on normal samples for training.
Among unsupervised approaches, memory bank-based methods have shown promise by maintaining repository normal features and computing anomaly scores based on their deviation from incoming test samples. To address spatial misalignment between test samples and stored prototypes, several recent methods [
universally adopt FPFH [ with RANSAC-based [9] coarse registration prior feature extraction. However, illustrated Figure significant residual misalignments often persist even after registration. While registration failures harm anomaly detection performance,
6 7
The first authors contributed equally.
Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a)
(b)
(c)
(d)
Figure 1: (a) Existing methods still exhibit misalignments after registration. (b) Visualization of PointMAE features the memory bank, with different colors for different objects. (c) Our method achieves accurate point cloud alignment. (d) Visualization of our locally discriminative features in the memory bank.
it also points to deeper problem: the feature encoders themselves lack the geometric sensitivity and transformation consistency required to support reliable anomaly detection.
This limitation stems largely from the nature of commonly used encoders such as PointMAE [T], which are designed to capture global semantics but often fail to preserve fine- the-grained local geom- etry(Figure [b)) and essential invariances. In particular, these encoders are not optimized to be inntionninvariant or to capture local structural nuances, both of which are essential for effective anomaly detection. As a result, feature representations struggle to maintain consistent correspon- dences under varying orientations and structural perturbations, especially in the presence of anomalies. When registration errors occur, these weaknesses exacerbate feature misalignment, ultimately result- ing in unreliable anomaly scores.
To address these challenges, we propose a shift in how registration is employed within the anomaly detection pipeline. Rather than using it as separate preprocessing module, we treat registration as an integral component of feature learning. Our key insight is that both point cloud registration and memory-based anomaly detection rely on the same core capabilities: modeling local geometric structures and capturing meaningful feature similarities across samples. Registration, by design, requires learning features that are rotation-invariant, locally sensitive, and structurally discriminative to establish accurate correspondences between source and target point clouds. Similarly, anomaly detection depends features that preserve fine-grained local details and remain consistent under rigid transformations, enabling precise comparisons between a test sample and normal prototypes stored in memory bank. By aligning feature learning with registration objectives, naturally yields representations well-suited for anomaly detection, overcoming the limitations of conventional encoders like PointMAE in handling geometric variations and structural defects.
approach
Building on this insight, we propose unified framework, called Reg2Inv, derive registration- induced rolation invariance for 3D anomaly detection. During training, the model learns features through a registration task that enforces both geometric alignment and multi-scale feature consis- tency between source and target point clouds. This process not only establishes accurate structural correspondences but also shapes the feature extractor to produce iitation-invarial and locally dis- criminative representations((ige point cloud, computes a registration matrix to align it with prototype, and then compares the normalized features to those in a coreset-sample memory bank. Anomaly scores are derived from these comparisons, allowing the system to identify local defects even under rotation or deforma- tion. By jointly optimizing feature learning and alignment, our method effectively addresses spatial misalignmentt(iiuum [ c)) and enhances feature robustness, enabling more reliable 3D anomaly detection. Experimental results Anomaly-Shapela and Real3D-AD confirm the superiority of our approach over existing baselines.
[d)) At inference time, the model extracts features from test
In summary, contributions are threefold:

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We delve into the intrinsic alignment between point cloud registration and memory bank- based 3D anomaly detection, and show that registration powerful feature learner for acquiring discriminative and cotation-invarian representations.
We propose unified framework Reg2Inv for egistration-induced rotation-invariant feature extrac- tion, which jointly performs accurate prototype-sampl alignment and enables effective anomaly scoring based on robust local geometric features.
We conduct extensive experiments two benchmark datasets, Anomaly-ShapeNe and Real3D- AD, demonstrating that our method consistently outperforms existing state-of-the-at approaches in 3D anomaly detection.
2 Related Work
2.1 2D Anomaly Detection
Anomaly detection in 2D images has seen significant progress in recent years, particularly under anomaly-free training settings. Popular approaches include feature embedding methods, among which flow-based, memory bank-based, and reconstruction-based techniques are widely adopted. Flow-based methods [TH model the distribution of normal features using normalizing flows and detect anomalies via likelihood estimation. Memory bank-based approaches (15)20 store features from pre-trained encoders and identify anomalies by comparing test samples to stored normal patterns. Reconstruction-batt anomalies through reconstruction errors. In this work, we focus on anomaly detection in 3D point clouds. Unlike structured 2D images, point clouds unstructured, unordered, and often sparse, posing greater challenges for feature learning and anomaly detection.
methods (21 Iearn to reconstruct normal inputs and detect
2.2 3D Anomaly Detection
3D anomaly detection targets structural irregularities in point-cloud data 27 28 and has seen increasing research attention. Existing methods generally fall into two categories: reconstruction- based and memory bank-based. Among reconstruction-basse methods, IMRNet (2 detects anomalies by reconstructing masked normal samples; R3D-AD (29] restores normal geometry from pseudo- abnormal inputs; and PO3AD (30) enhances local reconstruction by predicting offsets in defective regions. While these methods are effective at capturing fine-graine anomalies, they often suffer from
sensitivity to resolution and noise.
Memory bank- based methods aim to learn compact representations of normal structures for compari- son. Reg3D-AD I uses PointMAE [T0 to extract features and stores both features and coordinates in separate memory banks. Group3AD 6 introduces group-level feature aggregation strategy to improve anomaly sensitivity and alignment. ISMP [7] proposes an internal spatial modality perception framework that leverages spatial insight engine for enhanced feature discrimination. While these methods have shown promising results, they often overlook the limitations of current registration strategies and, more importantly, reflect deeper issue: feature representations frequently lack transformation consistency and local discriminability. In contrast, our work integrates point cloud registration into the anomaly detection pipeline to jointly optimize spatial alignment and feature learning. By aligning feature extraction with registration objectives, our framework generates rotation- ination-nvvariant and locally discriminative features, significantly enhancing detection robustness.
2.3
Coarse-to-Fine Point Cloud Registration
Coarse-to-fine strategies emerge as a compelling approach to capture hierarchical visual structure B1 32]. which also has been proven to be effective in both 2D image matching [33] and 3D point cloud registration 36 37]. Geometric Transformer [37], in particular, achieves robust registration by
incorporating geometric structure into attention- models. Our approach builds upon this line of work by not only employing coarse-to-fine registration pipeline but also enhancing with feature learning objectives that ensure both robust alignment and discriminative local feature extraction.
3

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Registration- Induced Feature Learning
Rutafion-eerariant Feature Extractor
Le
Geomeene Transommer
Confidence Marri
F
p2
Retstion Invariant Featuro Exuactic
Optera Transon
L
T
RIC CiConet
( PMA,
1 PM
[ po
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F
J
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Groeed with
Ceufidence Matris
Registration- Inndutiinn-n-uueee Anomaly
①
Righd trasforratin
A Comateraiz
Scarda and cocepezc ancesaly aCorE
Top-l
tran sample
Template
Memoryy Bank--
Rotation Invoriane Feanuee
Concirtio
Fetmet
Optinal Transport
Top=k
Ransze
sample
Filter
Estracter
Local
Sample
Top-l
Figure 2: The overview of Reg2Inv. In the feature learning stage, the model learns features via registration task that enforces geometric alignment and multi-scale consistency between paired point clouds. At inference time, the model computes registration matrix for alignment with the prototype and extracts rotation-invariant features for anomaly detection. The feature extractor is designed to obtain a of features for anomaly detection and two sets of features for registration.
3 Methodology
The 3D anomaly detection task involves a training set Duain = {P, E containing M normal objects and a test set Dies (Pr E R.V.333 | U (Pr e RNx33 consisting of J normal and K abnormal objects (where n and denote normal and abnormal, respectively). Each normal object pn contains only normal points p", while an abnormal object pd includes both p" and abnormal points p². The goal of this task is to train models on D rainn with two purposes: (1) object-level anomaly detection: distinguishing pnn and pa in Drest and (2) point-level anomaly localization: identify nd within pa to localize anomalies.
Problem statement.
x3
x31
Overview. As shown in Figure our framework Reg2Inv consists of two stages: Registration- Induced Feature Learning and Registration-Induee Anomaly Detection, both built upon Rotation- Invariant Feature Extractor. The model performs point-cloud registration in the feature learning stage by enforcing geometric and feature consistency across multi-scale patches and points. This refines the feature extractor to learn rotation-invariant and discriminative features while aligning structural correspondences. During anomaly detection, the model extracts rotation-invariant features from test samples, computes registration matrix to align them with prototype, and identifies anomalies by comparing normalized features coreset- sampled memory bank.
3.1 Registration-Induee Feature Learning
Point sampling & Ground-trute matches generation. In the feature learning stage, we first generate transformed pairs (P, Q) by applying random rigid transformations Tgt to each P e Derain yielding Q. Since both point cloud registration and anomaly detection benefit from reduced data complexity, and dense point clouds lead to redundant invalid point-wise alignments, employ multi-scale voxel downsampling on both P and Q. obtaining the first level points P, Q and the coarsest level points P. Q. Notably, P and Q are voxel downsampled from P and Q, their relationships be captured via point-to- point-to-node grouping strategy (3). Specifically, each P P is associated with its nearest coarsest-level point P, forming patches gP. The construction of go follows the same procedure applied Q and Q Formally, the patch gP is defined
g.P {p E P argmin, (1pp P)(l)), P, E P).
Given the gP and go, we construet two types of ground-truth matches between them enable supervised loss computation. Specifically, under the known rigid transformation Tg.. we compute the

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overlap ratio between patch pairs ( .g)). Patch pairs with an overlap ratio >> 0.1 are selected as ground-truft patch matches:
M ((9 Overlap(TT,( 0.1).
(2)
In addition, randomly sample N₂ patch pairs from Mus and establish ground-trui point matches by confidence threshold as follows:
M (( [[Tx(()) alle < t, PE G 9
where (G,, go E M and [anil No-
Features extraction. To leverage registration guidance for extracting discriminative and rotation- invariant features tailored to anomaly detection, we design a feature extractor comprising three components: RIConv++ [39]. KPConv-FPN backbone 40 41). and Geometric Transformer
module 37 with layer normalization. To capture fine fine-grained geometric details from P and Q,
we apply RICon+++ to extract dense local features features F" E [R xd" and F E Æl xd" where d" denotes the local feature dimension. These features encode rotation-invariant and geometri- cally coherent local structures, which are essential for anomaly detection. To encode hierarchical information across multiple resolutions, we employ the KPConv-FPN process multi-scale point clouds and generate multi-level point-wise features. The corresponding outputs for P and Q denoted F E IR |P]xdd and F E glêxxd xd' where d' denotes the point feature dimension. These features capture discriminative contextual patterns at the point level, enabling accurate and robust spatial alignment. Finally, p. and Q are processed using the Geometric Transformer module with layer normalization, yielding patch-wise features F E TR x and Få E IR []xd, where d is the final patch feature dimension. These features represent global structural priors that facilitate reliable registration under partial overlap sparsity.
Given these extracted features, for each patch GP, we use M to define its local feature matrix and point feature matrix as Fnr ; C FF"P and Fr? C F respectively. The local feature matrix p 2 and
point feature matrix è for each patch Gj are computed and denoted in a similar way.
Training objective To achieve accurate point cloud registration while simultancously leveraging the registration process to extract cotation-invariin features suitable for anomaly detection, our overall training objective formulated combination of three complementary components:
C Ly + Ep Loc-
Here, Ly is negative logeliiel loss [42] used for aligning local features. This loss key in encouraging the network to learn geometrically coherent and rotation-invariant representations, which are crucial for effective anomaly detection. For each ground-trute truth patch match (GP, 9%) Ma we first employ the optimal transport layer we then compute the corresponding cost matrix C" Subsequently, we augment each cost matrix C" by adding one row and one column filled with a learnable dustbin parameter a, forming C"r We then utilize the Sinkhorn [3] algorithm on C"* to obtain soft local feature assignment matrix Z The z" used to compute the Lf,, defined as:
extract local feature assignment matrix. from which
C..5= = Σ log !!" 1.T.
Σ log T
Σ VEJ
log
!!
(5)
im
(a.))EM
where M represents the set of matched points, while I, and Jj denote the sets of unmatched points in GP. and go. The L, computed by averaging the individual loss: L, = N₂
Cp and Loe are used to supervise point cloud registration at different levels of granularity. C, follows negative log-likelihood loss and shares similar computation process with L. It is tailored for point matching, refining point-level alignment, and enforcing accurate correspondences between individual points, which improves cross-cloud matching accuracy and enhances registration robustness.
5

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which prioritizes correspondences in regions with significant overlap. This helps improve the reliability of registration under partial overlap or sparse input by focusing on structurally consistent patches. Its definition is as follows:
Lee denotes the overlap-aware circle loss 37 44
log
[1
Σ E
N (4f (4)-
Σ
8th (An-
(6)
A
SPEA
Of
The synergy among these losses ensures accurate and robust registration, while promoting rotation- invariant and geometrically coherent representations, which are crucial for reliable anomaly detection. Further details on the loss formulations are provided in the appendix.
3.2 IibistrttionInducce Anomaly Detection
Point alignment. During the inference phase, a sample from Drain is selected as the template Q. and all samples in both Duain and Diest must undergo registration to align with this template. The same point sampling and feature extraction from training were applied to all samples. To find patch matches of each sample pair (P, Q). we compute Gaussian correlation matrix H. E R| with h4. cxp(- !F? F (12) as in [37. We then perform dual nommalammlii operation [35 45] on
H to obtain an augmented correlation matrix H. Finally, we select the largest N₂ entries H as the
patch matches:
M (Px: qy.) (24, yi) E topk2.9 (he.v))
For each match (G2, (e)) E M, we compute its point assignment matrix Z which is then recovered Z'; by dropping the last row and the last column. We select the largest entries Z': as the point matches:
M, = { (zy), og. (w:) (z,, E mutuall_oppe (=' (=',,..))).
(8)
The point matches computed from each patch match are then collected together to estimate the rigid transformation T by RANSAC (9. Subsequently, the estimated transformation T is applied to align P with Q:
Palignn = T(P).
Feature normalization and memory bank construction. For each train sample P E Duran we derive local features F"P E xxd" and the corresponding alignment coordinates Palign E R PP These are then aggregated across all training samples in Duain to form collections of feature vectors F and coordinate vectors Fe, from which we compute the normalization parameters Yy and Ye- respectively. The final representation F is obtained by normalizing F and F. using ? and 7es and then fusing them via concatenation-basee operator, that F ( Fa/Te)- Finally, we apply the Coreset sampling technique 15 46
xa"
construct the memory bank B.
Feature filtering and anomaly detection. For each test sample P E Diest we compute the final representation F in the same way. Feature filtering is performed to eliminate edge artifacts and ensure enhanced feature normalization across test samples. Specifically, we construct local neighborhoods around the feature-orrespooding points by computing Nr = KNN (p,, k), where P, P. We then calculate the centroid of each local neighborhood:
H
Σ Ps-
(10)
The feature filtering process be summarized by the following equation:
if ||p: -,u/l minpe., l|p 1ill
Filter(i)
(11)
otherwise
The filtered feature set F fa {ffs Filter (i) = 1} is subsequently employed for anomaly detection. The peint-level anomaly score s; defined as:
8 min llf
bbank 1l
(12)
bonk EB
where Si F ya- The object-level anomaly score S is computed by aggregating all point-level scores into a score mask and taking the maximum value after smoothing: S = max((s;) * fa), where fn mean filter of size and is the wisn convolution operator.

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Table 1: Comparison of AUROC results the object and point levels (%) of various methods on Real3D-AD. The best result in red and the second-best in blue for each category. (Raw) denotes the raw point coordinates used as input to the method. (FPFH) and (PMAE) denote configurations using Fast Point Feature Histograms [8] and PointMAR [0] as feature extractors. The top three methods are reconstruction-ben based, while the remaining ones are memory bank- based.
O-AUROC(T) / / (URROC(())
Method
Airplane
Car
Candy
Chicken
Diamond
Duck
Fish
IMRNet
76.2 /
71.1/
75.5/
78.0/
90.5 /
51.7/
88.0/
R3D-AD
77.2 /
69.3/
71.3/
71.4
68.5 /
90.9/
69.2/
PO3AD
80.4/
65.4/
78.5/
68.6/
80.1 /
82.0/
85.9
BTF(RAW)
73.0/ 56.4
64.7/64.7
73.9/
68.9
70.7 / 56.3
69.1 60.1
50.2/
(TF(FPFH)
52.0/73.8
56.0/
63.0/
43.2
54.5 / 88.2
78.4/ 87.5
70.9
M3DM
43.4/54.7
54.1/60.2
55.2/67.9
6
60.2 / 60.8
43.3/ 66.7
$4.0/60.6
(FtC)C)r(FFPFH)
88.2/56.2
59.0/75.4
54.1/78.0
83.7/42.9
57.4 82.8
26.4
6 82,9
PatchCorc(PMAAB)
72.6/56.9
/9.8
66.3/62.7
82.7/72.9
78.3 71.88
52.9
63.0/71.7
CPMF
70.1/61.8
55.1/83.6
55.2/73.4
50.4/55.9
/5.33
58.2/71.9
/
Reg3D-AD
63.6/63.1
69.7/713
68.5/72.4
90.5/83.5 67.2/
/
91.5/ 82.6
GrDup3AD
73.6
72.8/74.5
84.7/73.8
93.2/ 78.6/75.9
67.9/ 63.1
97.6/ 83.6
ISMP
85.8/75.3
73.1/834
85.2/90.7
71.4
94.8/92.8
71.2/ 87.6
94.5/ 88.6
Ours
81.8/92.3
75.87 94.4
100. 796.9
94.4/9100
100. 197.9
75.07 93.7
67.2 84.6
Method
Gemstone
Seahorse
Shell
Starfish
Toffees
Average
IMRNet
67.4/
60.4/
66.5
67.4/
77.4/
72.5 /
R3D-AD
66.5 /
72.0/
84.0/
70.1
70.3
73.4/
PO3AD
69.3 /
75.6/ 80.0
75.8
77.1/
76.5
BTF(RAW)
59.7
59.67 52.0
39.6 48.9
53.0 39.2
70.3 / 62.3
/
BTF(FFFH)
64.8/89.1
71.9/5.2
57.1
50.1
46.2 81.5
60.3 73.33
M3DM
64.4/67.4
49.5/
69.4/73.8
55.1/53.2
4 68.2
63.1
PatchCore(FPFH)
37.0/91.0
50.5/73.9
58.9/73.9
56.5 60.1/
/9.3/68.2
PatchCorePMAEE)
37.4/44.4
53.9/63.3
50.1/70.9
51.9/58.0
58.5/
59.4/22.0
CPMF
58.9/44.9
72.9/96.2
65.3/72.5
70.0/
39.0/95.9
58.6/
Reg3D-AD
41.7/
76.2/8177
58.3/81.1
50.6/61.7
82.7/75.9
70.4/70.5
Group3AD
53.9/56.4
84.1/82.7
58.5
52.2/
79,6/80.3
75.1 73.55
ISMP
72.9/81.3
63.3/8399
66.0/64.1
89.5
75.7 83.6
Ours
73.5 90.7
64.2
69.2/90.6
84.1784.0
62.6/73.7
78.0/87.8
4 Experiments
4.1 Experimental Settings
Datasets. Evaluation is conducted on Anomaly-ShapeNet 2 and Real3D-AD 1. Anomaly- ShapeNet is synthetic 3D anomaly detection dataset with 1,600 samples across 40 categories, each containing 4 normal samples in the training set. Real3D-AD is real-world high high-resolution dataset with 12 object categories, each category has 4 normal training samples and 100 test instances. To simulate practical scenarios, training samples in Real3D-AD are captured via full 360 scans, while test samples are collected from single-view perspectives.
Evaluation metrics. We evaluate anomaly detection performance at both the object and point levels using the Area Under the Receiver Operating Characteristic Curve (AUROC). Object-level AUROC (O-AUROC) measures the effectiveness of anomaly detection, while Point-level AUROC (P-AUROC) assesses localization accuracy. Higher values in both metrics indicate stronger anomaly detection and localization capabilities.
Details. We set the local feature dimension to 32. For adaptive voxclization. the voxel size is dynamically determined via binary search to maintain 8, 3,192 sampled points on Real3D-AD and 4,096 on with an initial learning rate le-4. The learning rate is first linearly warmed up for 10k steps, then decayed following cosine schedule to 10% of the initial value. Experiments conducted single RTX 4090D GPU.
The model is trained for 100k iterations using the Adam optimizer
Baselines. Our method is evaluated against two categories of state-of-the-art 3D anomaly detection approaches on Shapeeee:: (1) Reconstruction-bbsee methods, including IMRNet 2 R3D-AD 29 and PO3AD [30]; and (2) Memory bank-based methods, including BTF 47].
7

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Table 2: Comparison of AUROC results the object and point levels (%) of various methods on -eaaay-SSaaeNee......
O-AUROC(T) / (OAL)
ashiaao
bago
bontet)
bottlel
botke3
bowl)
bowil
bowi3
bowi4
bowts
IMRNaT
67.1
6607666
$5.2/55.6
64.066411
68.178.1
70.27705
68.5768.4
59.9/599
67.6/57.6
71.0/
R3D-AD
83.31
72.07
73.3/
73.2/
78.17
N1.9
7
76.77
74.47
65.67
PO3AD
100./96. 796.2
83.3/949
90.0/91.2
93.3/64.4
92.6788.0
92.2/99.8
82.9/9144
8331/918
88. 89.1/93.5
98. 98.179677
84.9/94.1
BYFIRAW
57,8751.2
410772.0
39.7755.1
51.07
36,577220
56,4/52.4
26.47564
525742.6
36.5706.3
66,4756.3 56.3
41.7:517
8TP))))))))
62.0/62.
54.6/74.6
34.4/64.1
54.6/54
32.2/62.2
50.971.0
66.8
510//518
49.0/590
60.916799
609/000
59.7/57.7
53.7/63.
57.4/66.3
63.7/6377
153.2
63.4/65.8
66.3
084/6994
61.7/653
46.46624
42.9448.
Prcchaes
58.7/59.7
57.1
60.4/654
66.7/68-7
57.2/51.2
63.0/51.1
61.5
53.7/
40.4/73.0
55.8/358
59.1749.5
60.1787
51.3/55.3
60.17
65.0165.3
52.3/52.7
62.9/524
50.1/50.1
CPMP
35.3/61.5
48.2/57.1
66.1
68.3768.3
RogaD-AD
59.7/098
7066 71.5
48.6/884
09.5
52.5752.5
/7.1/775
63.5
4910/ 59.3
34.87654 7554
66.3/5001
59.3/69.1
ISMP
100.3
177.0
1568
177.5
/85.1
754.6
177.3
174.1
753.
Ours
06.0778.5
100.
100 799.5
100. 7849
100 7817
7983
807/828
6566
58.57661
85.277888
8187824
Mathod
buskol
buckett
expo
csp5
cujo
cupi
emsero
headseth
headser
TMRNa
58.0
/774
73.7/715
77.5/ 7016
65.2775.3
65.2/743 /74.2
64.37643
7537688
5487548
72.07 70.5
6767476
R3D AD
68.37
75.67
82.2/
73.0/
68.17
67.0/
77.6
75.2
89.0/
73.87
79.5
PO3AD
35.3
76.7 89.9
87.3/95.7
85.9/918
79.296.0
67.0 96.4
87.1/9099
63.3/93.
99.5797.4
92.37 80.6/52.
90.7
BVFIRAW
61.7761.7
$2.17 68.6
06.87324
52.77 68.7
46.874669
37.3
20336312
52.1756.1
52.766.7
37,875188
57.37.7.55
RTFF)PHH)
40.1/40.1
63.3 13.3/6333
61.8/73.0
52.2 62.2/65588
52.0152.4
58.6 58.6
58.6 79.0
61.0/619
71//7119
$2.01 62.0
40.07 99.1
MRDM
30.9/69988
50.1 6699
55.7/.5..1
/2.3/60.5
77.7171.8
66.5
53.9/71.5
55.6/55.
62.7/71..
$7.71 58.1
55.5
PorchCorrFFPEEE
44.9/45.9
55.1 57.1
54.0/47.2
45.3/65.3
75.7159.5
79.0 79.5
60.0/65.5
58.6 59.6
68.7/81.0
58.37 58.3
63.7/46.5
PoschooerPMMA))
59.3/58.5
56.1 /5.44
754.
47.6/ 85.8
72.7172.5
53.8
61.0/510
55.6/85.6
/
59.1/57.5
62.7/ 42.3
CPMP
48.2/48.5
60.1 60.1
00.1/60.1
55.1/55.1
55.3155.3
09.7/55.1
/9.7
09.9
/6.9
64.37 69.9
45.8
RoA
61.0/619
75.2/75.3 75.2
09.3/63.
72.5/718
64.37815
46.7/4677
51.0/6855
34.3/ 75.5
53.7/58.0
61.0/626
ISMP
/52.4
167.2
186.5
7734
175.3
167.8
186.9
760.
1 7706
158.0
/70.2
Ours
$1,3761,0
90.2/855
65.9/8611
8633794
68.17864
90.2/97.0
73.3/798
93.3888
100, 796.0
790.794.6
$4.3797.0
Mithod
helmett
helmetl
helmer2
helmet3
ju0
phone
shel00
Eip0
txp
vaxeO
YIM1
IMRNet
59.7/59.8
60.0 60.4
64.1/644
57.3/663
78.0776.5
75.5/74.2
60.3/805
67.6
69.67
69.9
53,3/50.5
75.7
R3D-AD
73:71
72.07
63.3/
70.21
83.81
76.2/
/
73.6
90.07
78887
72.91
POSAD
76.2/87.8
96.17941 /91.
36.9/93.2
75.4/84.6
86.5/88711
77.6 81.0
57.3 66.3
74.5 74.5/78.3
78.3
68.1 69.2
83.8/95.5
74.21 88.2
55,3/50,4
54.97449
/605
52.6/700
42.0742.3
56.3/56.
16.4726 26.4
52.5
53.17618
54.95449
ATF(FMF
57.1/57.5
71.9/749
54.2 64.3
42.4
42.4-42.7
59.6
34.2764.2
21.9/61.9
M3DM
52.6/59.9
42.7
62.3/
37.4/65.5
154.1
38.7 38.8
56.4/55.1 55.4
75.41
54.6/54.8
68.9
42.57 45.5
40.47
48.8
/1.3
76.6/ 76.3
45.5168.5
PosshCoe(PMMAE)
55.6/58.0
55.2 56.2
44.7/ 65.1
42.4/61.5
48.3748.7
18.8/ 58.6
52.3/54.3
45.8 18.
13.8/54.1
44.7/67.7
55.2/55.1
CPMP
55.5/55.5
58.9 54
46.2/5155
52.0055..
61:01611 1
50.9 54.5
68.
68.5/78.3
35.7455
69.7/65.7
45.1/
44.5/43.6
ADD
60.0/60.0
38.1
63.4
6182
66.7/62.0
59.3159.9
41.47 99.9
68.8 68
67.67 58.9
64.1 74.1
53.31
70.3/6022
ISMP
/68.3
62.2
186,
772.2
182.3
166.1
768.7
152.2
/55.2
7 /66.
1
Ours
$1.7/92.5
90.6
87.5789.1
67.67958
100, 198.2
100, 199.2
57.7/6322
9487918
80.47 86.9
99.67 99.00
60.5/705
Method
vase2
Yase3
vased
vane5
10967
vase8
vase?
IMRNet
61./6144
70.0/0011
52.4/224
68.6/68.2
53.5/59.3
63.0/63.5
/ 89.1
65.1/55.0
RED-AD
63.07
71.8/
95.2/97.8
67.5/90.3
85.2
73.0/95.0
85.0/952
BYFRRAW
41.0/40.3
71.7/002
42.5/61.3
58.5/58.5
46.8:77.8
42.4/55.0
56.4/56.4
49.3755.0
BTFIFPTTT)
54.6/64.66
69.9 89.9
51.0/71.0
40.9/ 42.9
51.8156.0
66.8/66
26.8/66.8
52.8/62.8
M3DM
73.7/33.7
43.9/65.8
47.6/65.5
31.2/64.2
65.7/51.7
66.3/55.1
66.3/66.3
55.2/61.6
P
72.1/22.1
43.0
50.5/50.5
41.3 44.7
69.3169.3
66.2/57.5
66.0/66.3
56.8/58.0
74.1/74.2
46.0/46.5
51,6/52.3
57.9/57.2
65.0765.1 1
66.3/36.
62.9/423
/
CPMP
58.2/58.2
58.2/58.
514/514
61.8/6.1
39.71504
$2.9/52.
609/51.5
55.9/57.3
RoD
60.5/40.5
65.0/5111
50.0/75.5
52.0/ 62.4
46.2/88.1
62.0/811
594/894
57.2/56.
1SMP
173.3
154.5
/47.2
15.1
161.5
31.5/92.7
7879
81.87934
87.3/971
66.788.2
M3DM [88, PatchCore [5), CPMF 49
Reg3D-AD M. and ISMP (7]. On Real3D-AD, we
additionally compare with Group3AD 6
Performance metrics for all compared methods
obtained from their original publications or publicly available implementations.
4.2 Quantitative results
4.2.1 Results on Real3D-AD
Table 1 presents the comparison of object-level and point-level anomaly detection performance on Real3D-AD. According to the average scores, our method achieves the best results on both metrics, outperforming the second-bess best method by 1.5% in O-AUROC and 4.2% in P-AUROC. Notably, our method attains the highest P-AUROC score in 8 categories and ranks second in one category, demonstrating its strong localization capability and confirming that the features learned by our model are highly discriminative.
4.2.2 Results on Anomaly-ShapeNee
Table summarizes the results of anomaly detection and localization on method achieves the best performance on O-AUROC, outperforming the second-best by 2.2%, and ranks second on P-AUROC with only a 1.6% gap to the top. Notably, Our approach significantly outperforms all memory bank-based methods, including Reg3D-AD and ISMP, on both O-AUROC (by 28.9%) and P-AUROC (by 19.1%), and achieves the best performance across 30 object categories.
Our
8

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(a) (b)
(c)
(d)
(c)
Figure 3: Visualization comparison localization results on Real3D-AD. (a) Ground truth. (b) Ours.
(c) PatchCore using FPFH and PointMAE, respectively. (d) ISMP. (f) Reg3D-AD.
These results show that our method learns rotation-invariant features with strong robustness and local discriminability, leading to more reliable 3D anomaly detection. The comparisons confirm its effectiveness and superiority, especially within memory bank-based frameworks.
4.3 Qualitative Results
As shown in Figure our method generates sharper and more accurate anomaly maps on Real3D-AD, with detected anomalies closely aligning to the ground-truth defect regions. This leads to a significant improvement in P-AUROC compared to alternative approaches. Additional qualitative results provided in the appendix.
4.4 Ablation Study
We conduct ablation studies on the registration strategy, training objective, memory bank feature composition, and performance under noisy point clouds to evaluate their respective effects. Additional ablation results are provided in the appendix.
Evaluation on registration. We eval- uate the impact of different registration methods on anomaly detection perfor- mance using the Real3D-AD dataset.
Table 3: Ablation study on registration strategy.
PatchCore (RAW)
PatchCore (PMAE)
Regis.
Reg3D-AD
Ours
As shown in Table 3 two key insights emerge. First, when registration method is applied, all downstream ap- proaches achieve improved P-AUROC scores, demonstrating that it provides more accurate and stable aligmment for anomaly localization compared the commonly used FPFH+RANSAC. Second, even when using FPFH+RANSAC for registration, our method still out- performs competing approaches, indicating that our feature representations possess stronger rotation invariance and robustness properties essential for reliable 3D anomaly detection.
F+R
65.3
64.2
70.5
72.6
Our
66.6
75.6
79.6
87.8
Evaluation training objective. To assess the roles of Loc. Ly. and Cy. we performed detailed ablation study the Real3D>AA dataset. As shown in Table [4] Loo is crucial for accurate registration, effectively aligning local patches even under partial overlap or sparsity, and its removal causes severe degradation. L enhances feature robustness by strengthening rotation invariance and local discriminability, thereby improving overall anomaly localization accuracy. L, has limited effect on accuracy but ensures efficient and reliable point matching; without it, inference slows due to numerous spurious matches. Overall, Loe and L, enable accurate and stable detection, while Cp improves efficiency, jointly balancing performance and computational cost.
Table Ablation study on training objective.
loss
O-AUROC
P-AUROC
w/o Loc
58.1
68.1
w/o Cp
76.9
87.0
wlo L,
73.4
86.0
Lue Lg + L. 78.0
87.8

---

Table Ablation study on memory bank feature composition. Anomaly ShapeNet
Real3D-AD
Coord.
Feat.
O-AUROC
P-AUROC
Coord.
Feat.
O-AUROC
P-AUROC
70.3%
66.2%
80.5%
67.2%
V
76.4%
83.7%
82.4%
85.0%
78.0%
87.8%
86.1 %
88.2 %
Table 6: Ablation study on noisy point clouds.
Setting 1
Setting
Metric
O-AUROC
P-AUROC
Noise
O-AUROC
P-AUROC
Clean
78.0
87.8
Clean
78.0
87.8
SD=0.001
76.7
87.5
SD=0.001
76.9
86.5
SD=0.003
77.7
86.4
SD=0.003
63.0
69.1
SD=0.005
76.8
83.1
SD=0.005
56.6
56.5
Evaluation memory bank feature composition. We evaluate the contribution of each memory bank feature component on Real3D-AD and Anomaly-ShapeNet. As shown in Table coordinates (Coord.) and local features (Feat.) play complementary roles. Coord. provides global spatial cues, while Feat. captures fine-grained geometric details. Their combination achieves the best performance, showing strong synergy: Coord. locates anomalies globally, and Feat. enhances local discrimination. This is especially evident in geometric anomaly detection, where the combined use improves P-AUROC by 21.6% (Real3D-AD) and 21.0% Annoall-SShpeeNe over Coord.-only baselines.
Evaluation on noisy point clouds. We evaluate the robustness of method noisy point clouds on the Real3D-AD dataset under two settings. In Setting 1, Gaussian noise (SD= 0.005) is injected during training, with varying noise levels applied during testing. In Setting noise is introduced only at test time. As shown in Tables the model trained without noise augmentation (Setting 2) exhibits clear performance drop at higher noise levels (SD 2 0.003), revealing its sensitivity to input perturbations. In contrast, noise-based augmentation during training (Setting 1) allows the model maintain stable performance across different noise intensities.
5 Conclusion
In this paper, we propose Reg2Inv, a unified framework that leverages registration learn rotation- invariant features for 3D anomaly detection. We identify key limitations in existing encoders, such as their poor preservation fine-grained local geometry and lack rotation invariance. To address these issues, integrate registration into the feature learning process, enabling the extraction of structurally discriminative and transformation alignment and multi-scale feature consistency within unified training objective, our method produces features suitable for both registration and anomaly detection. Extensive experiments validate the effectiveness of Reg2Inv in achieving accurate registration and reliable anomaly detection.
representations. By jointly optimizing geometric
Limitation. Despite promising results, our approach has several limitations. Training a model per class increases computational and storage costs. In less discriminative classes, the ootation-ivaaiian feature extractor shows limited generalization. In addition. registration is not always accurate for anomaly detection. We plan to address these issues in future work to improve the method's efficiency and practicality.
Acknowledgement. This research is supported by the "Leading Talent" under Guangdong Special Support Program (2044TX0X0048). China National Key R&D Program (2044FB44709000, Key- Area Research and Development Program of Guangzhou City (No.2023B0110022), Guangdong Provincial Natural Science Foundation for Outstanding Youth Team Project (No. 1024B15150000 NSFC Key Project (No. U23A20391), Guangdong Natural Science Funds for Distinguished Young Scholars (Grant 2023B151 500000997), the Singapore Ministry of Education AcRF Tier Grant (Grant No.: MSS25C004), and the Lee Kong Chian Fellowships
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Appendix
A Method Details
A.1 RIConv++
a rotation-invarian convolution method designed for deep learning on 3D point RIConv++ 39
clouds. It proposes an Information-Be -icch Invariant Feature (IRIF) formulation that not only captures the relationship between the central point and its neighbors but also encodes the internal geometric relations among the neighboring points, thereby enhancing feature discriminability.
A.2 Training objective details
Cy for local feature alignment and Cp for point matching. For each ground-truth patch match (.. M two parallel optimal transport layers [42] are used to extract local feature as- signment matrix and point assignment matrix, respectively. Specifically, first compute cost matrices:
C² = F (F"2) ) v
/dd"", 2
(13)
C² F p (F' v: y² /d'
where C" and C'; E RT GP, 12.. -- The cost matrices C"; are then augmented to C"² by appending new and a new column, filled with learnable dustbin parameter We then utilize the Sinkhorn (43 algorithm on C", to compute a soft local feature assignment matrix Z"; The point assignment matrix Z is computed similarly. To prioritize local feature alignment and point matching, we apply separate negative log-likelihood losses
[42 to the assignment matrices
z and Z The set of matched points is M The sets unmatched points in the two patches are denoted I₁ and J.. The individual local feature aligning loss L.f and point matching loss Lp. computed as:
Σ (
log 2"
Σ aEI;
log z" i,,m;:11
Σ VET;
log
= im
(14)
i.
Cps =
Σ (R.))
o log
Σ ZET
log La,,
Σ yET,
log zt 1,g1
(15)
The final losses are computed by averaging the individual loss: LG Cy..
1 N,
E
Lyf Lp =
N₂ EN
Loc for patch matching. To prioritize high-overlap matches for point cloud registration, we compute the overlap-aware circle loss [37 44 on P.
C
log 1
X (4) Ap).
Σ
8th (An-d)))
(16)
GPEA
GPEEh
G Ect
Where A is the set anchor patches in that have at least one patch pair in Me! For each anchor patch G. A, and = denote the sets of its positive and negative patches in Q. df || -FO1 l| is the feature distance, and X = (of) and of represents the overlap ratio between G.P and Go. gir <dd - Ap) and 3i.k = >An, dt) are the positive and negative weights, respectively. We set the margins A, = 0.1 and A, = 1.4. The same applies to the loss L in Q and the overall loss of the circle based on overlap is Cor = (cP, + /2
p
The loss function L = L, Cp + Loe is composed of feature aligning loss L, for local feature aligning, a point matching loss Lp for point matching, and an overlap-aware circle loss foe for patch matching.
21

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Table 7: Comparison of inference speed.
Metric
RegAD
M3DM
ISMP
Ours
AITPS
7.71
6.43
4.37
2.53
A.3 Normalization parameters compute
For each train sample P Durain derive local features FuP E R P and the corresponding
aligmment coordinates palign E R|P1x3, These are then aggregated across all training samples in Dusin to form collections of feature vectors F, and coordinate vectors F from which we compute the normalization parameters y and Yc. respectively:
Yf
F
12. F
U M FUF
(17)
U Balign
No [I...(
2, F
where M denotes the total number of samples in Darin and represents the dimensionality of the feature vectors.
A.4 AUROC
We evaluate anomaly detection performance both the object and point levels using the Area Under the Receiver Operating Characteristic Curve (AUROC). The AUROC quantifies the overall discriminative capability of a model. Given prediction scores s(x) for samples x, the true positive rate (TPR) and false positive rate (FPR) decision threshold are defined as
TP(T)
FP(T)
TPR(T) TP(T) - FN(()'
(PR(T) FP(T) + TN(())
(18)
The Receiver Operating Characteristic (ROC) curve plots TPR(T) against FPR(T) over all thresholds
E [0, 1]. The AUROC is defined the area under this curve:
AUROC- [ TPR(FPR) d(FP)).
(19)
In discrete form, the AUROC can be approximated as
N4 IE( s(5)),
AUROC- N =1
(20)
where N4 and N_ denote the number of positive and negative samples, and II(-) is the indicator function.
A.5 Training Efficiency
Our method learns features via a registration task that enforces geometric alignment and multi- scale consistency between source and target point clouds. As it does not rely category-specifie supervision, is inherently category-agnostic and can trained jointly across the entire dataset. Under experimental setup (RTX 3090 GPU, batch size 1), full training takes approximately 27 hours on Anomaly-ShapeNee (40 categories) and about 34 hours on Real3D-AD (12 categories). In contrast, methods like R3D-AD and PO3D-AD require I hour and 7 hours per category, respectively. While our single training run is longer, it eliminates the need for category-wise training, resulting in significantly greater overall efficiency.
A.6 Inference Efficiency
To assess runtime efficiency, we report the average inference time per sample (AITPS, measured in seconds). Using an RTX 3090 GPU with a batch size of 1, our method achieves an average processing time of 2.53 seconds per Real3D-AD sample, which is faster than other memory bank- based approaches, as shown in Tables 7
22

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B More Ablation Studies
To better understand the role of different design choices in framework, perform ablation studies on four critical components: (i) the computation method for object-level anomaly scoring; (ii) the neighborhood size in RiConv++; (iii) normalization and filtering of features during inference; (iv) using alternative extracted features for memory bank construction. These ablations provide insights into how each component contributes overall performance and robustness. Additionally, to evaluate the robustness of our method, we conduct experiment with varying rotation angles of point clouds, demonstrating its stability under different geometric transformations.
B.1 Evaluation the calculation method of object-level anomaly score.
We evaluate the impact of different strate- gies for computing the object-level anomaly dataset.
Table 8: Ablation study on calculation method.
score(%) the We compare two standard aggregation meth- ods: mean, defined as S mean ((s;) }), and max, defined as = mar( (fx;) Our pro- posed method, denoted ours, is formulated as S = max( {s,} } * fn). where 8; is the point-level anomaly score, fn is a mean filter of size n, and is the point-wise convolution operator. As shown in Table our method achieves the highest O-AUROC score among all aggregation strategies. Unlike conventional mean or max operations, our approach first applies local smoothing followed by operation, which effectively highlights true anomalous regions while reducing the impact of noise and unreliable point-level predictions.
Method
mean
max
ours
O-AUROC
81.6
82.8
86.1
B.2 Evaluation on neighborhood size in RIConv++.
Figure 1 shows object-level and point-level AU- ROC results with varying neighborhood sizes in RIConv++, evaluated the Real3D-AD dataset. Selecting an appropriate neighborhood size is
90
AUROCP
80
critical: an excessively large size may dilute ge- ometric patterns by aggregating diverse points, weakening local feature discrimination; con-
70
Object- level
versely, overly small size may limit contex- tual coverage, reducing the expressiveness of lo- cal structures. Despite these effects, our method remains relatively robust to neighborhood size choices. As shown in Figure 4 detection and localization performance peak at patch numbers 32 and 64, respectively. We set the patch num- ber to 32 in our implementation to prioritize detection performance, with only a minor trade-off in localization accuracy.
Point-level
60
32
64
128
neighborhood size
Figure 4: Ablation study on neighborhood sizes.
B.3 Evaluation Normalization & Filter in inference phase.
We evaluate the impact of feature normalization and filtering strategies during the inference phase on both object-level and point-level anomaly detection performance(% using the Real3D-AD dataset. As shown in Table normalization emerges as the key component driving performance improvement. Feature normalization alone improves O-AUROC by 10.5% and P-AUROC by 21.6%. In contrast, filtering plays a more auxiliary role. Applying filtering alone results in only marginal gains, but when combined with normalization, significantly enhances performance, achieving an O-AUROC improvement of 17.9% and a P-AUROC improvement of 22.5%. These results highlight the importance of proper feature conditioning during inference and suggest synergistic effect between normalization and filtering in improving model reliability and sensitivity.
23

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Table 9: Ablation study on Normalization Filter.
Normalization
Filter
O-AUROC
P-AUROC
60.1
65.3
62.9
65.7
70.6
86.9
78.0
87.8
B.4 Evaluation alternative feature sources for memory bank construction
We conducted an ablation study on alternative feature representations for memory bank construction using the Real3D-AD dataset. As shown in Table T0 KPConv-FPN features F' are suboptimal due to limited receptive fields and weaker semantics.
Table 10: Ablation study on the memory bank construction.
Memory Bank Construction
O-AUROC
P-AUROC
F palign +
51.1
43.8
F
54.0
56.1
F"
76.4
83.7
palign
F"
78.0
87.8
B.5 Evaluation on rotated point clouds
We conducted rotation experiment the Real3D-At dataset to directly demonstrate the enhanced rotation invariance and robustness of our learned features. At test time, each point cloud is randomly rotated around one of the X, Y. or Z axes by 90", 180% or 270". We then evaluate the performance of the model using only the features learned in these rotations. As shown in Table 11 the model achieves consistent performance all angles, confirming that the learned features possess strong rotation invariance and generalizability.
Table 1I: Results under varying levels of rotation.
Rotation Angle
P-AUROC O-AUROC
0"
76.4
83.7
90º
76.3
83.9
180
76.2
83.6
270º
76.6
84.3
C More Qualitative Results
We present additional qualitative results to further demonstrate the anomaly localization capability of our method. Tigure[]ssows the results the Real3D-AD dataset, while Figures 6 and 7 display the results on the Shomaly-ShapeNNe dataset. These visualizations highlight that our method not only accurately identifies anomalous regions but also produces spatially coherent anomaly scores, effectively distinguishing between normal and defective structures in 3D point clouds.
24

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Ground truth
Anomaly map
Ground truth
Anomaly map
Ground truth
Anomaly
map
Figure 5: More qualitative results of localization on the Real3D-AD dataset.
25

---

Ground truth
Anomaly map
Ground truth
Anomaly Ground map truth
Anomaly map
Ground truth
Anomaly map
Figure 6: More qualitative results of localization the hnomaly-Shapenee dataset.
26

---

Ground truth
Anomaly map
Ground truth
Anomaly map
Ground truth
Anomaly map
Ground truth
Anomaly map
Figure 7: More qualitative results of localization on the Anomaly-Shapene dataset.
27


Reviews:
[{"review_id": "WiejYg4SVA", "reviewer": "Reviewer_PScz", "summary": "This paper tackles the task of 3D point cloud anomaly detection by introducing a registration learner built upon a memory bank. Specifically, the authors train a rotation-invariant feature extractor using point cloud registration as the learning objective, enabling accurate alignment between test samples and templates during inference, while also extracting effective local features. Quantitative results demonstrate the effectiveness of the proposed method.", "questions": "1. How long does it take to train the proposed method?\n\n2. What is the inference speed in frames per second (FPS)?\n\n3. Are there any hyperparameters in the proposed method? If so, the authors are expected to conduct a sensitivity analysis to evaluate the robustness of their choices.\n\n4. How does the proposed method perform on noisy point clouds? A discussion or experiment on robustness to noise would strengthen the paper.\n\n5. Do “Coord.” and “Feat.” correspond to L_p, L_oc, and L_f, respectively? If not, an ablation study explicitly analyzing the contributions of these three losses is recommended.", "limitations": "YES", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n* The idea of integrating a point cloud registration module into memory-bank-based method is reasonable, which is empirically demonstrated by authors.\n* The writing of this paper is clear and easy to read.\n* The authors conduct extensive experiments to demonstrate the effectiveness of their method.\n\nWeaknesses:\n\n* I appreciate that authors provide a detailed overview of the proposed method. However, the Figure 2 is somewhat complicated, the authors are expected to simplify it to help readers better understand.\n* The authors are expected to provide the equation of L_oc in the main text.", "was_revised": true, "final_justification": "Thanks for the authors' response. After reading the rebuttal as well as the comments from the other fellow reviewers, I believe the paper is a good contribution to the community. As such, I would like to keep my previous positive rating.", "rebuttal": "#### **Point-by-Point Response to Reviewer PScz**\nWe sincerely appreciate your thoughtful and constructive comments, which have greatly enhanced the quality of our manuscript. Below, we provide detailed responses to each of your questions.\n\n> **Question-1:** *\" Figure 2 is somewhat complex and should be simplified to improve readability. \"*\n> \n> **Response:** Our intention was to align the architectural diagram with the methodology by illustrating the training/inference procedures, as well as the Rotation-Invariant Feature Extractor. We acknowledge the resulting complexity and will simplify Figure 2 by removing unnecessary lines, standardizing dashed styles, and eliminating redundant labels.\n\n> **Question-2:** *\" Provide the equation for $\\mathcal{L}\\_{oc}$ in the main text. \"*\n>\n> **Response:** We have provided the equation for $\\mathcal{L}\\_{oc}$ in the Supplementary Material and will move it to the main text in the revision.\n\n> **Question-3:** *\" How long does it take to train the proposed method? \"*\n>\n> **Response:**  Our method learns features via a registration task that enforces geometric alignment and multi-scale consistency between source and target point clouds. As it does not rely on category-specific supervision, it is inherently category-agnostic and can be trained jointly across the entire dataset. Under our experimental setup (RTX 3090 GPU, batch size 1), full training takes approximately 27 hours on Anomaly-ShapeNet (40 categories) and about 34 hours on Real3D-AD (12 categories). In contrast, methods like R3D-AD and PO3D-AD require ~1 hour and ~7 hours per category, respectively. While our single training run is longer, it eliminates the need for category-wise training, resulting in significantly greater overall efficiency.\n\n> **Question-4:** *\" What is the inference speed in frames per second (FPS)? \"* \n>\n> **Response:** Thank you for the suggestion. While FPS is less commonly used for point cloud anomaly detection due to variable point counts and the absence of a clear “frame” definition, we report **average inference time per sample** as a practical alternative. Under our setup (RTX 3090 GPU, batch size 1), our method takes an average of 2.53 seconds to process one sample on the Real3D-AD dataset, outperforming other memory bank-based methods, as shown in **Table 1** below.\n> \n> **Table 1:** Comparison of Inference Speed.\n> | Method | average inference time per sample (seconds) |\n> | :---: | :---: |\n> | RegAD | 7.71 |\n> | M3DM | 6.43 |\n> | ISMP | 4.37 |\n> | **Ours** | **2.53** |\n\n> **Question-5:** *\" Does the proposed method involve hyperparameters, and has a sensitivity analysis been conducted to assess their robustness? \"*\n>\n> **Response:** Yes, our method involves a key hyperparameter: the neighborhood size in RIConv++. To assess its impact, we conducted a sensitivity analysis on the Real3D-AD dataset, as shown in Figure 1 of the supplementary material. The results indicate that smaller neighborhood sizes limit contextual information, while larger sizes may weaken local feature discrimination by aggregating dissimilar points. Despite these effects, our method performs consistently well across a range of values, demonstrating robustness to this hyperparameter.\n\n> **Question-6:** *\" How does the proposed method perform on noisy point clouds? \"*\n>\n> **Response:** We appreciate the reviewer’s insightful comment. Like most models, ours is sensitive to unexpected noise during testing. To assess robustness, we conducted experiments on the Real3D-AD dataset under two settings. In Setting 1, Gaussian noise (SD = 0.005) was added during training, with various noise levels applied during testing. In Setting 2, noise was added only during testing. As shown in **Tables 2 and 3** below, the model without noise augmentation (Setting 2) shows a noticeable performance drop at higher noise levels (SD ≥ 0.003), indicating sensitivity to input perturbations. However, with noise-based augmentation during training (Setting 1), the model maintains stable performance across different noise levels. These results suggest that, with minor fine-tuning, our method can effectively handle noisy point clouds.\n\n> **Table 2:** Results of Setting-1\n> | noise | O-AUROC | P-AUROC |\n> | :---: | :---: | :---: |\n> | clean | 78.0 | 87.8 |\n> | SD=0.001 | 76.7 | 87.5 |\n> | SD=0.003 | 77.7 | 86.4 |\n> | SD=0.005 | 76.8 | 83.1 |\n>\n> **Table 3:** Results of Setting-2\n> | noise | O-AUROC | P-AUROC |\n> | :---: | :---: | :---: |\n> | clean | 78.0 | 87.8 |\n> | SD=0.001 | 76.9 | 86.5 |\n> | SD=0.003 | 63.0 | 69.1 |\n> | SD=0.005 | 56.6 | 56.5 | \n\n> **Question-7:** *\" Do \"Coord.\" and \"Feat.\" correspond to $\\mathcal{L}\\_{p}$, $\\mathcal{L}\\_{oc}$, and $\\mathcal{L}\\_{f}$, respectively? If not, an ablation study explicitly analyzing the contributions of these three losses is recommended. \"*\n>\n> **Response:** Thank you for the question. \"Coord.\" and \"Feat.\" refer to the registered point coordinates and the learned local features, respectively, but they do not directly correspond to $\\mathcal{L}\\_{p}$, $\\mathcal{L}\\_{oc}$, and $\\mathcal{L}\\_{f}$. To clarify their contributions, we conducted an ablation study on these three loss terms using the Real3D-AD dataset, with results shown in **Table 4** below.\n\n> The analysis shows that each loss serves a distinct role:\n\n> 1. $\\mathcal{L}\\_{oc}$ is the most critical for accurate registration, enabling the model to align structurally consistent local patches even under partial overlap or sparsity. Removing it severely degrades performance.\n\n> 2. $\\mathcal{L}\\_{f}$ enhances the rotation invariance and local discriminability of features, directly improving anomaly localization accuracy.\n\n> 3. $\\mathcal{L}\\_{p}$ has a smaller impact on detection accuracy but is important for efficient point matching. Without it, inference time increases significantly due to many spurious matches.\n\n> In summary, $\\mathcal{L}\\_{oc}$ and $\\mathcal{L}\\_{f}$ primarily support accurate feature learning and detection, while $\\mathcal{L}\\_{p}$ improves inference efficiency. All three contribute to balancing performance and computational cost. We will include these discussions in the revision.\n\n> **Table 4:** Ablation study on loss components.\n> | loss components | O-AUROC | P-AUROC |\n> | :---: | :---: | :---: |\n> | w/o $\\mathcal{L}\\_{oc}$ | 58.1 | 68.1 |\n> | w/o $\\mathcal{L}\\_{p}$ | 76.9 | 87.0 |\n> | w/o $\\mathcal{L}\\_{f}$ | 73.4 | 86.0 |\n> | $\\mathcal{L}\\_{oc}$ + $\\mathcal{L}\\_{p}$ + $\\mathcal{L}\\_{f}$ | 78.0 | 87.8 |"}, {"review_id": "5FUKFQsYN0", "reviewer": "Reviewer_BTG1", "summary": "The authors propose Reg2Inv, an anomaly detection framework where the core insight is the connection between anomaly detection and point cloud registration. In the first stage, the model learns feature via a registration task, while in the inference stage, rotation-invariant features are extracted and used in a memory-bank-based anomaly detection approach. The proposed approach achieves excellent results on standard datasets.", "questions": "- In the ablation study, Table 3: How come the Coord works fairly well? It does not seem to contain that much information intuitively.\n- What is the intuition behind using F’’ and the P^align coordinates as the features in the coreset? Why not other extracted features?", "limitations": "The authors have adequately addressed the limitations.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- Network is trained for registration which then learns to extract features that perform well in memory-based anomaly detection models. The insight that performing registration produces features that are discriminative enough for anomaly detection is interesting. \n- The proposed method is conceptually simple but interesting and is based on a novel insight. \n- The paper is generally very well written and easy to follow.\n\nWeaknesses:\n- The main major weakness is the somewhat weak ablation study. No ablation experiments in the paper for Loss components, filtering, using other extracted features for memory bank computation etc. \n- Lacking intuition for some design choices such as various loss components, filtering etc. Essentially things that are missing from the ablation study. I know these are present in the quite extensive supplementary material but it would still be useful for the reader to see more of these in the main paper.", "was_revised": true, "final_justification": "Most of the issues I've raised have been addressed in the rebuttal. I maintain my score.", "rebuttal": "#### **Point-by-Point Response to Reviewer BTG1**\nWe sincerely appreciate your insightful comments, which significantly improved our paper. In response, we provide our detailed replies to your questions as follows:\n\n> **Question-1:** *\" The main major weakness is the somewhat weak ablation study. No ablation experiments in the paper for Loss components, filtering, using other extracted features for memory bank computation, etc. \"*\n> \n> **Response:** We thank the reviewer for the valuable feedback on the ablation study. We would like to clarify that several ablations addressing key components of our method are included in the supplementary material. Specifically, Table 1 presents the ablation of the loss component $\\mathcal{L}\\_{f}$, highlighting its critical contribution to detection performance. Table 2 reports the impact of the filtering strategy, which serves an auxiliary function.\n>\n> Additionally, we conducted an ablation study on alternative feature representations for memory bank construction using the Real3D-AD dataset. As shown in **Table 1** below, KPConv-FPN features $\\mathbf{F}'$ are suboptimal due to limited receptive fields and weaker semantics.\n> \n> **Table 1:** Ablation study on the use of extracted features for memory bank construction.\n> | Memory Bank Construction | O-AUROC | P-AUROC |\n> | :---: | :---: | :---: |\n> | $\\mathbf{F}'$ | 51.1 | 43.8 |\n> | $\\mathcal{P}^{align}$ + $\\mathbf{F}'$ | 54.0 | 56.1 |\n> | $\\mathbf{F}''$ | 76.4 | 83.7 |\n> | $\\mathcal{P}^{align}$ + $\\mathbf{F}''$ | 78.0 | 87.8 | \n\n> **Question-2:** *\" Lacking intuition for some design choices such as various loss components, filtering, etc. Essentially, things that are missing from the ablation study. I know these are present in the quite extensive supplementary material, but it would still be useful for the reader to see more of these in the main paper. \"*\n>\n> **Response:** We appreciate the reviewer’s valuable feedback on the presentation of key design choices. The losses $\\mathcal{L}\\_{p}$ and $\\mathcal{L}\\_{oc}$ are standard in point cloud registration, while $\\mathcal{L}\\_{f}$ specifically supervises the RIConv++ module in learning the target features. The filtering mechanism is designed to remove edge responses and suppress false positives. To maintain clarity and conciseness within the paper's space constraints, we focus on the core contributions in the main text. We will expand on these design choices in the main text and direct readers to the supplementary materials for a more detailed analysis and experimental validation.\n\n\n> **Question-3:** *\" In the ablation study, Table 3: How come the Coord works fairly well? It does not seem to contain that much information intuitively. \"*\n>\n> **Response:** While point coordinates may seem limited in expressiveness, they are a fundamental geometric property of point clouds, capturing 3D positions and encoding global shape information. In memory bank-based anomaly detection, coordinates are commonly used for patch matching and similarity comparison. The good performance of \"Coord.\" in Table 3 results from our method’s precise registration, ensuring consistent coordinate correspondence across point clouds, which makes coordinate-based comparisons effective even without additional features.\n\n> **Question-4:** *\" What is the intuition behind using $\\mathbf{F}''$ and the $\\mathcal{P}^{align}$ coordinates as the features in the coreset? Why not other extracted features? \"* \n>\n> **Response:** In constructing the coreset, we chose to use feature $\\mathbf{F}''$ and aligned coordinates $\\mathcal{P}^{align}$ for the following reasons: $\\mathbf{F}''$ offers strong rotational invariance and high local resolution, effectively capturing local geometric patterns, while $\\mathcal{P}^{align}$ provides 3D position and global shape structure, offering both spatial priors and a structural reference. This combination balances local detail and global consistency, enabling the model to detect subtle local anomalies while maintaining robust geometric coherence.\n>\n> We did not use $\\mathbf{F}'$ or $\\mathbf{F}$, as they are designed for key point matching to compute the rotation matrix for registration. Their limited receptive fields and weaker semantic representation make them less suitable for capturing the broader geometric context required for effective anomaly detection."}, {"review_id": "JICiNDuE14", "reviewer": "Reviewer_iakd", "summary": "This paper proposes a 3D industrial anomaly detection method called Reg2Inv. This approach achieves better registration effects and ensures more balanced coverage of discriminative features in the memory bank. The proposed method was evaluated on the Real3D-AD and Anomaly-ShapeNet datasets, and was compared against both recently proposed and classic approaches, demonstrating certain advantages.Additionally, ablation studies were conducted on the key modules to evaluate their individual contributions.", "questions": "About the generalizability of registration-based methods. Is the proposed registration based method still maintains effectiveness in 2D scenarios (e.g., Real-IAD dataset) or multimodal settings (e.g., MVTec3D-AD) ?", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength: \n\n1.Overall, 3D anomaly detection holds considerable research value.\n\n2. The motivation behind the proposed method is both reasonable and highly interesting. Registration indeed plays a critical role in anomaly detection, and the consideration of disriminative features coverage across the entire object in the memory bank is technically sound. Notably, this is the first work I've encountered that addresses this specific aspect, demonstrating clear innovativeness.\n\n\nWeakness\n\n1.The overall clarity and readability of the method presentation need improvement, particularly in Figure 2's framework illustration. The Rotation-Invariant Feature Extractor is an independent module that doesn't necessarily need to be included in Figure 2, as this unnecessarily increases the diagram's complexity. Additionally, the excessive use of different dashed line types makes the figure difficult to interpret, especially in printed versions.\n\n2.In the comparative experiments, the proposed method demonstrates certain advantages over other methods, though the improvements are not statistically significant.\n\n3. The presentation of experimental tables has significant issues. Table 1 and Table 2 compare too many methods, resulting in fragmented content that compromises clarity. The Experiment Setting section should clearly specify which representative methods are truly worthy of comparison.\nRegarding Table 2, the Anomaly-Net results include excessive object categories that unnecessarily complicate the main text. The paper should focus on reporting average metrics in the main content while moving detailed breakdowns to supplementary materials. Additionally, the experimental analysis needs strengthening to better contextualize these results.\nTable 3 suffers from an awkward format, combining two internally unrelated tables into a single presentation. This layout choice hinders clear interpretation of the results.", "was_revised": false, "final_justification": "", "rebuttal": "#### **Point-by-Point Response to Reviewer iakd**\nWe sincerely appreciate your insightful comments. We believe some concerns may stem from misunderstandings, which we clarify below.\n\n> **Question-1:** *\" The overall clarity and readability of the method presentation need improvement, particularly in Figure 2's framework illustration. \"*\n>\n> **Response:** Our intention was to align the architectural diagram with the methodology by illustrating the training/inference procedures, as well as the Rotation-Invariant Feature Extractor. We acknowledge the resulting complexity and will simplify Figure 2 by removing unnecessary lines, standardizing dashed styles, and eliminating redundant labels.\n\n> **Question-2:** *\" In comparative experiments, the proposed method shows some advantages over others, though the improvements are not statistically significant. \"*\n>\n> **Response:** We respectfully disagree with the statement that the improvements are not statistically significant. As shown in **Tables 1 and 2**, our method achieves clear and consistent performance gains over existing approaches across both benchmarks: \n>\n> * On **Real3D-AD**, our method achieves an average **O-AUROC of 78.0%** and **P-AUROC of 87.8%**, outperforming the second-best with **76.5% / 83.6%**, a margin of **+1.5% and +4.2%**, respectively. \n> * On **Anomaly-ShapeNet**, our method achieves **86.1% / 88.2%**, compared to **83.9% / 89.8%** by PO3AD. These improvements in object-level AUROC reflect stronger detection ability on clean and complete shapes.\n>\n> We believe these gains are significant and reflect the robustness and effectiveness of our approach. Moreover, reconstruction-based methods like PO3AD rely on complete geometry and cannot operate on partial or occluded point clouds. As a result, they struggle to achieve satisfactory performance when computing point-level AUROC on Real3D-AD and are limited to classification only. In contrast, our method processes incomplete inputs and supports both classification and localization, offering broader applicability in real-world scenarios.\n>\n> In summary, the consistent performance advantage, along with greater flexibility, demonstrates the practical strength and generalization ability of our method.\n\n\n> **Question-3:** *\" The presentation of experimental tables has significant issues in Tables 1-3, including overcrowding with too many methods, overly complex content, and suboptimal formatting. Additionally, the experimental analysis needs strengthening to better contextualize these results. \"*\n> \n> **Response:** We thank the reviewer for pointing this out. While the design of Tables 1 and 2 follows common practices in prior work, we agree that the large number of object categories may make the tables appear visually dense. However, this level of detail is necessary for a fair and comprehensive comparison. To aid interpretation, we would like to emphasize that the **average scores at the end of each table provide the most intuitive summary of overall performance**, and these averages are the key indicators we highlight in the analysis.\n>\n> To further improve clarity, we will restructure Tables 1 and 2 by grouping methods by category and simplifying the layout of Table 2. Table 3 will be split into two separate tables for easier readability.\n>\n> In addition, we will expand the experimental analysis with more focused discussion to better contextualize the results and clearly highlight the strengths of our method.\n\n\n> **Question-4:** *\" Is the proposed registration-based method still maintains effectiveness in 2D scenarios(e.g., Real-IAD dataset) or multimodal settings (e.g., MVTec3D-AD) ? \"*\n> \n> **Response:** We thank the reviewer for raising this question, and we discuss it from two perspectives to provide a comprehensive response：\n>\n> 1. **Application to 2D Anomaly Detection:** 2D registration typically involves local, pixel-wise alignment, making it challenging to estimate a reliable global transformation between images captured from different viewpoints. Without consistent global alignment, holistic feature extraction becomes unreliable, and the benefits of 3D registration cannot be achieved. As such, our registration-based approach is not directly applicable to 2D anomaly detection.\n>\n> 3. **Application to Multimodal Anomaly Detection:** For multimodal anomaly detection on the MVTec3D-AD dataset, which provides an RGB image and a corresponding point cloud from a single viewpoint, anomaly detection can be performed on the point cloud using our registration-based method, with the results subsequently fused with those from the image modality. However, as MVTec3D-AD provides only partial point clouds, this prevents the use of a complete shape template, potentially leading to less stable registration.\n\n\n**To Reviewer iakd:** The primary concern raised appears to be that our performance improvements are not statistically significant. However, this may stem from overlooking the average performance metrics reported in each table, where our method consistently achieves clear and meaningful gains over all baselines. The other concerns, such as the complexity of Figure 2 and the formatting of the tables, are relatively minor and can be readily addressed during revision.\n\nGiven that the core issues raised are either not substantiated by the data or are minor in nature, we believe the negative evaluation is not justified. We have addressed all points thoroughly in our rebuttal and sincerely request a reconsideration of the decision in light of the significant, consistent, and practically meaningful improvements demonstrated by our method."}, {"review_id": "YrYuB21bpD", "reviewer": "Reviewer_Zbiu", "summary": "This paper introduces a novel framework called Reg2Inv that integrates point cloud registration with feature learning to achieve rotation-invariant and locally discriminative representations for 3D anomaly detection. The key idea is to leverage the alignment process of registration to guide feature extraction, ensuring that the learned features are robust to rotations and effective for identifying structural defects. Experimental results demonstrate that RegInv outperforms existing methods on the Anomaly-ShapeNet and Real3D-AD datasets.", "questions": "According to weaknesses, my questions are:\n1. The introduction and related work sections could provide a more thorough discussion of the limitations of existing methods and how Reg2Inv addresses these limitations. This would help readers understand the significance of the contributions more clearly.\n2. The evaluation metrics lack detailed explanations. It is recommended to supplement with calculation formulas and the set thresholds.\n3. The paper compares its method with several state-of-the-art approaches but does not provide a detailed analysis of why Reg2Inv outperforms these methods. A deeper comparative analysis, including qualitative results and discussions on specific cases where Reg2Inv excels, would enhance the validation of the claims. \n4. The authors indirectly verify that the learned features possess stronger rotation invariance and robustness properties through the ablation study on registration. However, this is not intuitive. It is suggested that the authors apply different levels of rotation to the two datasets and test the performance changes of different methods.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The writing quality of the paper is excellent. The authors clearly articulate the problem, review related work, and present their methodology logically and coherently.\n2. The idea of this paper is interesting. Using registration to learn rotation invariance for anomaly detection is intuitive and in line with the direction of research development.\n\nWeaknesses:\n1. The visualizations in Fig. 1 are not aligned.\n2. The evaluation metrics are not described clearly.\n3. Experiments lack detailed analysis of why Reg2Inv outperforms these methods.", "was_revised": true, "final_justification": "My concerns regarding the experimental results have been addressed. I plan to raise my score to borderline accept.", "rebuttal": "#### **Point-by-Point Response to Reviewer Zbiu**\nWe sincerely appreciate your insightful comments. We believe some concerns may stem from misunderstandings, which we clarify below.\n\n> **Question-1:** *\" The visualizations in Fig. 1 are not aligned. In addition, the introduction and related work sections could provide a more thorough discussion of the limitations of existing methods and how Reg2Inv addresses these limitations. \"*\n>\n> **Response:** We thank the reviewer for the comment. However, it appears there may have been a misunderstanding regarding the role of **Figure 1**, as well as the depth of discussion provided in the manuscript.\n>\n> **Figure 1, along with the second through fourth paragraphs of the Introduction**, was specifically designed to articulate the motivation behind our work and to highlight the limitations of existing methods. In particular, this section discusses two critical challenges:\n>\n> 1. **Unreliable Registration:** Traditional methods fail under large pose variations (Fig. 1(a)), preventing accurate spatial alignment.\n> 2. **Weak Feature Representation:** Methods like PointMAE lack local discriminability and rotation invariance (Fig. 1(b)), leading to inconsistent features and degraded anomaly detection.\n>\n> Our method, **Reg2Inv**, is explicitly proposed to overcome these challenges by jointly optimizing registration and feature learning to produce robust, locally discriminative features (Fig. 1(d)), improving both alignment (Fig. 1(c)) and detection performance.\n>\n> We welcome constructive suggestions to improve the clarity and precision of our writing. However, a single, high-level comment without concrete guidance does not provide actionable feedback for revision and is unlikely to benefit the submission or the broader research community. We are fully committed to improving the manuscript and would greatly appreciate more specific comments on how to strengthen the discussion or presentation.\n\n\n> **\"Question-2:\"** *\" The evaluation metrics lack detailed explanations. It is recommended to supplement with calculation formulas and the set thresholds. \"*\n>\n> **Response:** Thank you for the suggestion. The evaluation metrics we use are standard and widely adopted in the literature, and, in line with common practice, we did not include the calculation formulas in the main paper to preserve space and maintain clarity. However, as suggested, we will provide detailed formulas and threshold settings in the supplementary material.\n\n> **\"Question-3:\"** *\" The paper compares its method with several state-of-the-art approaches but does not provide a detailed analysis of why Reg2Inv outperforms these methods. A deeper comparative analysis, including qualitative results and discussions on specific cases where Reg2Inv excels, would enhance the validation of the claims. \"*\n>\n> **Response:** We respectfully disagree with this comment. Due to space constraints, we have made our best effort to provide a detailed comparative analysis throughout the main paper and supplementary material. In addition to the experimental section, we have integrated comparative insights into **Figure 1**, **Figure 3**, and conducted **extensive experiments in the supplementary material**, all of which collectively explain why Reg2Inv outperforms existing methods.\n>\n> Specifically, Reg2Inv excels by effectively capturing discrepancies in both coordinates and features:\n>\n> 1. **Improved Registration Accuracy:** As shown in **Fig. 1(a) and (c)**, Reg2Inv achieves better alignment, refining spatial correspondence and reducing false geometric deviations. This allows coordinate differences to more faithfully reflect structural anomalies.\n>\n> 2. **Enhanced Feature Discriminability:** As demonstrated in **Fig. 3**, Reg2Inv produces locally discriminative features that remain consistent in normal regions and highlight anomalies more clearly. Competing methods tend to exhibit missed detections or false positives due to weaker local feature modeling.\n>\n> 3. **Rotation Invariance without Memory Bank:** **Table 3** in the main text shows that Reg2Inv outperforms memory bank-based methods using only local features, confirming its robustness to rotation and strong feature consistency.\n>\n> 4. **Comprehensive Ablation Studies:** The supplementary material includes **extensive ablation studies** that evaluate the individual contributions of each component in our framework, further validating the effectiveness of our design choices.\n>\n> We appreciate the suggestion and will further strengthen this analysis in the revised experimental section. However, we believe the current version already provides a thorough and well-supported explanation of Reg2Inv’s advantages.\n\n\n> **\"Question-4:\"** *\" It is not intuitive to verify that the learned features possess stronger rotation invariance and robustness properties through the ablation study on registration. It is suggested that the authors apply different levels of rotation to the two datasets and test the performance changes of different methods. \"*\n>\n> **Response:** We acknowledge that the ablation study on registration does not directly demonstrate the enhanced rotation invariance and robustness of our learned features. To address this, we conducted a rotation experiment on the Real3D-AD dataset. At test time, each point cloud was randomly rotated around one of the X, Y, or Z axes by 90°, 180°, or 270°. We then evaluated the model’s performance using only the learned features under these rotations. The results in **Table 1** show consistent performance, confirming that the learned features possess strong rotation invariance and generalization ability.\n>\n> **Table 1:** Results under varying levels of rotation.\n> | rotation angle | O-AUROC | P-AUROC |\n> | :---: | :---: | :---: |\n> | 0° | 76.4 | 83.7 |\n> | 90° | 76.3 | 83.9 |\n> | 180° | 76.2 | 83.6 |\n> | 270° | 76.6 | 84.3 |\n\n\n\n**To Reviewer Zbiu:** We sincerely thank you for your time and thoughtful comments. However, it seems that some concerns may stem from a partial misunderstanding of our work. In particular, the key issues raised regarding the level of comparative analysis and discussion appear to overlook the efforts we have made throughout the manuscript, including detailed explanations in the main text, visual illustrations in Figures 1 and 3, and extensive experiments and ablation studies in the supplementary material. \n\nWe fully respect the importance of constructive critique and welcome suggestions that help improve the quality of the work. However, some of the comments are quite general and would benefit from more specific guidance to better serve both the revision process and the research community. \n\nWe have carefully addressed all concerns in our rebuttal and sincerely hope that, with these clarifications, the contributions of our work can be reconsidered in a fair and balanced manner."}]

Author rebuttal:

Meta-review: The main idea of the paper is to integrate the objectives of point-cloud registration and memory-bank-based 3D anomaly detection. The proposed framework utilizes registration learning to derive rotation-invariant and locally discriminative features.

The paper initially received mixed reviews. It had one rating of "accept," one rating of "borderline accept," and two ratings of "borderline reject." The main issues noted in the reviews include the clarity of figures and tables, the length of training time and inference speed, and the need for analysis of sensitivity and robustness. Additionally, concerns were raised about the weak ablation study, a lack of intuition behind some design choices, and insufficient detailed analysis of the experiments.

In response, the authors provided explanations and additional experiments to address these issues. After the rebuttal, one reviewer who had initially rated the paper as "borderline reject" upgraded the score to "borderline accept." Another reviewer commented, "I believe the paper is a good contribution to the community." None of the reviewers expressed any further concerns. As a result, the overall ratings shifted toward a more positive outlook.

The area chair does not see any significant flaws and considers the remaining issues, such as visualization and analysis, to be minor and easily addressable. The area chair concurs with the reviewers' assessments and recommends accepting the paper. Additionally, the area chair has discussed the evaluations and recommendations regarding this paper with the senior area chair to ensure that all perspectives have been fairly considered in the decision-making process.
Decision: Accept (poster)