Title: Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Point Cloud

Anomaly Detection
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nAccording 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength: \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.\n\nQUESTIONS:\nAbout 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) ?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:
- 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. 
- The proposed method is conceptually simple but interesting and is based on a novel insight. 
- The paper is generally very well written and easy to follow.

Weaknesses:
- 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. 
- 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.

QUESTIONS:
- In the ablation study, Table 3: How come the Coord works fairly well? It does not seem to contain that much information intuitively.
- 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.
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-01

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:

* The idea of integrating a point cloud registration module into memory-bank-based method is reasonable, which is empirically demonstrated by authors.
* The writing of this paper is clear and easy to read.
* The authors conduct extensive experiments to demonstrate the effectiveness of their method.

Weaknesses:

* 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.
* The authors are expected to provide the equation of L_oc in the main text.

QUESTIONS:
1. How long does it take to train the proposed method?

2. What is the inference speed in frames per second (FPS)?

3. 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.

4. How does the proposed method perform on noisy point clouds? A discussion or experiment on robustness to noise would strengthen the paper.

5. 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
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03