Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

Bingshu Wang; Can Gao; Chengbin Hou; Guoyang Xie; Hanzhe Liang; Jinbao Wang

REVIEW 4 major objections 6 minor 46 references

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Looking inside point clouds, not just at their surface, yields state-of-the-art 3D anomaly detection on Real3D-AD.

desk verdict A simple internal-slicing idea shows real gains on Real3D-AD, but sloppy reporting and a tautological proof need fixing before the SOTA claim is credible. read the letter →

arxiv 2412.13461 v2 pith:4UHEAAAO submitted 2024-12-18 cs.CV cs.AIeess.IV

Hanzhe Liang , Guoyang Xie , Chengbin Hou , Bingshu Wang , Can Gao , Jinbao Wang This is my paper · ORCID

classification cs.CVcs.AIeess.IV

keywords 3Danomalydetectionpointcloudinternalspatialpseudo-modalityInsightEngineReal3D-ADlocalizationfeaturefilteringAUROC

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that 3D anomaly detection methods that only view the external surface of a point cloud are missing discriminative internal structure. It introduces ISMP, which uses a Spatial Insight Engine to project the point cloud into four internal spatial pseudo-modalities, and combines them with enhanced local features and a Laplacian-based feature filter. On the Real3D-AD benchmark, ISMP raises object-level AUROC by 3.2 points and pixel-level AUROC by 13.1 points over prior state-of-the-art. The central claim is that internal views carry information about defects that external views cannot reveal, and the experiments support this by showing the internal slices alone outperform an external-only variant.

What carries the argument

The Spatial Insight Engine (SIE) is the load-bearing component. It converts a registered point cloud into four 2D pseudo-images: one top-down projection and three internal slices that bisect the cloud along the z-axis, encoding depth from the midpoint toward the top or bottom. These pseudo-images are fed to a pre-trained image encoder (EfficientNet) to produce global features, which are then aligned with local patch features from Farthest Point Sampling, PointMAE, and FPFH descriptors, and finally passed through a Laplacian-based feature filtering module that suppresses redundant information. The resulting feature matrix is compared against memory banks of normal training samples to compute point-level anomaly scores.

What would settle it

Train ISMP on Real3D-AD with the two internal slices P2 and P3 replaced by slices at randomly chosen z-depths (for example, at 30% and 70% of the height range), keeping all other components fixed. If the randomized-slice variant matches or exceeds the original P-AUROC of 0.836, then the specific mid-height partition is not the source of the improvement, and the claimed 'internal information' is an artifact of having more projections.

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Extended reading notes

Core claim

The paper's central discovery is that internal spatial pseudo-modalities, generated by slicing a point cloud at its top, two middle planes, and bottom, provide a richer global feature representation for anomaly detection than external-only projections. The authors formalize this with an information measure $I_{\text{global}} = I_{\text{top}} + \sum_{i: z_i \geq z_{\text{mid}}} (z_i - z_{\text{mid}}) \geq I_{\text{top}}$, where the internal slices add non-negative depth-based terms to the top-down projection. Empirically, using only the two internal middle slices already outperforms an external-only variant, and the full SIE with all four slices yields the best accuracy, supporting the claim that looking inside contributes genuine signal. The method also demonstrates that the internal pseudo-modality generalizes to point cloud classification and segmentation, where injecting SIE features into a standard point cloud backbone yields small but consistent gains.

Load-bearing premise

The assumption that the internal slice features are actually discriminative for anomalies, rather than merely adding non-negative numbers to an information measure, is the point on which the method's motivation rests.

Editorial extensions

If this is right

Future 3D anomaly detection can be built around single-sensor point clouds without needing aligned RGB-D or multi-view data, since internal views come from the same point cloud.
The SIE's generalization to classification and segmentation suggests the internal pseudo-modality is a reusable representation for point cloud understanding beyond defect detection.
The large pixel-level AUROC gain indicates that internal slices help localize anomalies, which could directly aid industrial inspection and robotic quality control.
The method operates from as few as four normal training samples per category, making it applicable to production lines where defect data is scarce.
The feature filtering module's controllability over feature mean and variance gives practitioners a principled way to tune detection sensitivity.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper's information-theoretic justification ($I_{\text{global}} \geq I_{\text{top}}$) is a tautology because it follows from summing non-negative terms; it does not prove the internal depth terms are discriminative, so the empirical gains are the real evidence.
A testable extension is to apply SIE to symmetric or textureless objects where internal geometry is the only cue, to see if internal pseudo-modalities alone can detect subtle deformations.
The idea of 'internal slicing' could be transferred to voxel grids or meshes, potentially improving other 3D representation learning tasks.
Ablating the image encoder to a lighter network could reduce the inference overhead while preserving most of the benefit, as the current FPS numbers show SIE is slower than some baselines.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the internal-slicing idea is genuinely new for 3D anomaly detection, and the ablation suggests it does real work. But the paper undercuts itself with two different O-AUROC numbers in the abstract, main text, and Table 1, and the 'theoretical proof' of SIE's superiority is a tautology. Worth refereeing, but not as-is.

What's actually new: instead of projecting the point cloud from the outside, SIE slices it along the z-axis into top, bottom, and two internal partitions, then feeds those pseudo-images into an EfficientNet backbone. That's a simple, transferable idea. The local features combine PointMAE patches with FPFH descriptors around FPS centers, and the Laplacian filtering is a reasonable denoising step. The ablation in Table 3 is the most informative part: removing the internal slices drops O-AUROC by roughly 10 points, which is the strongest evidence that the internal signal matters. The generalization experiments on ModelNet40 and ShapeNet-Part are a nice extra, even if the gains are small.

Soft spots, in order of seriousness. First, the numbers don't match: the abstract says 3.2% O-AUROC improvement, the experimental section says 0.757, and Table 1's mean is 0.767. That 0.757-vs-0.767 discrepancy is exactly the difference between a 3.2% and a 4.2% gain, so it's not cosmetic. Second, Eq. (5) is just saying that adding non-negative terms to a sum doesn't make it smaller. It does not prove that internal slices carry discriminative anomaly information. The ablation does that job, but the paper used Eq. (5) as if it were a proof, which is misleading. Third, the hyperparameters α, β, γ are chosen by inspecting feature-mean/variance heatmaps on Real3D-AD itself. With only four normal training samples per category, that's effectively tuning on the target benchmark. No error bars, no code, no held-out validation protocol. These problems don't sink the empirical claim, but they do mean the SOTA claim is not yet robustly established.

Who this is for: the 3D anomaly detection community, specifically people working on Real3D-AD and pseudo-modal approaches. The paper is clearly written and the method is easy to describe, so a serious referee could get through it quickly. The verdict should be 'revise' rather than 'reject', with the numerical inconsistency and the hyperparameter selection protocol as the main requirements.

Recommendation: send it to peer review, but require the authors to reconcile the reported numbers, show error bars or a justification for the discrepancy, and either move hyperparameter selection to a validation split or otherwise show it isn't inflating the results.

Referee Report

4 major / 6 minor

Summary. The paper proposes ISMP (Internal Spatial Modality Perception) for 3D anomaly detection. The method's central component, the Spatial Insight Engine (SIE), projects a registered point cloud into four slices (P1-P4) and uses internal slices as pseudo-modalities; an enhanced feature extraction branch combines PointMAE patch features with FPFH descriptors, and a graph-Laplacian-based feature filtering module suppresses redundant information. Anomaly scores are computed from nearest-neighbor distances in feature and coordinate memory banks. On Real3D-AD the paper reports O-AUROC 0.767 and P-AUROC 0.836 in Table 1 (with the abstract claiming 3.2% and 13.1% improvements), P-AUROC 0.691 on Anomaly-ShapeNet in Table 2, and auxiliary classification and segmentation experiments for SIE on ModelNet40 and ShapeNet-Part. The authors argue that internal information is richer than external projection and state that the generalization ability of SIE has been theoretically proven.

Significance. If the empirical results hold, the paper makes a useful contribution: it demonstrates that internal projection slices can serve as pseudo-modalities for 3D anomaly detection, with particularly strong pixel-level localization gains on Real3D-AD. The ablation study (Table 3) and the generalization experiments (Tables 5 and 6) provide practical evidence that the modular components, including SIE, are not obviously harmful and may help on standard benchmarks. The use of public benchmarks is another strength. However, the analytical support for the central novelty is currently only an inequality that holds by construction, the headline O-AUROC numbers are mutually inconsistent, and the parameter-selection protocol does not fully exclude test-set influence. These issues need to be resolved before the state-of-the-art claim is fully credible.

major comments (4)

[Method / Spatial Insight Engine, Eq. (5)] The derivation in Eqs. (1)-(5) shows only that I_global = I_top + sum_{i: z_i >= z_mid}(z_i - z_mid) >= I_top, which is true by construction because all added terms are non-negative. This does not establish that the internal z-discrepancy carries anomaly-discriminative information, and Eq. (6) is a heuristic threshold rule rather than a proof. Since SIE is the paper's central novelty and the abstract claims that its generalization ability has been theoretically proven, this is a load-bearing gap: please either provide a genuine argument (e.g., under stated assumptions, internal slices separate anomalous from normal points), or remove/reword the theoretical claims.
[Abstract and Experiments / Main Results vs Table 1] The reported O-AUROC numbers are mutually inconsistent. The abstract claims a 3.2% improvement, the main text reports ISMP with O-AUROC 0.757, and Table 1(a) lists a mean O-AUROC of 0.767 for ISMP versus 0.725 for IMRNet, which corresponds to a 4.2 percentage-point improvement. Please correct the numbers and state clearly which mean (0.757 or 0.767) is the official result and against which baseline the improvement is measured.
[Experiments / Ablation Study and Figure 4] The hyperparameters alpha=0.2, beta=0.2, gamma=0.001 for the feature filtering module are justified by inspecting mean/variance heatmaps of feature matrices (Figure 4), and the same parameter values are then used to obtain the final Real3D-AD results. The text suggests the heatmaps are computed on synthetic feature matrices resembling PointMAE features, but no separate validation split is used for parameter selection, and each category has only four normal training samples. This creates a risk of inadvertent test-set tuning that can inflate the reported gains and confounds the attribution of improvements to SIE and the filter. Please justify parameter selection on a held-out validation protocol, report sensitivity of the main results and the ablation to these parameters, and provide variance or confidence intervals over multiple runs or splits.
[Abstract and Evaluation of SIE Generalization] The abstract states that the strong generalization ability of SIE has been theoretically proven, but no theorem, proof, or formal statement appears anywhere in the manuscript; Tables 5 and 6 provide empirical demonstrations only. Please either supply the proof or remove the claim, and if the proof is deferred to a supplementary document, cite it explicitly.

minor comments (6)

[Method / Anomaly Score Calculation, Eq. (13)] The typeset formula for s_F appears to be missing a closing parenthesis or brace; please rewrite it unambiguously and define all symbols, including P(x_test) and N_3(m*).
[Method / Enhanced Feature Extraction] The patch radius r in Eq. (8) and the number of FPS center points m are never specified in the experimental details; please state their values and the patch size used.
[Method / Feature Filtering Module, Algorithm 1] Lines 16-17 normalize the filtered feature matrix by its maximum; please clarify whether this normalization is applied per test sample at inference time and justify its effect on nearest-neighbor distances in the memory banks.
[Method / Feature Filtering Module] The text refers to the Laplacian transform, but Eq. (9) defines the graph Laplacian matrix L = D - A; please align the terminology with the actual linear-algebraic object being used.
[Figure 2 and Figure 4] Figure 2 is difficult to read: the projection slices P1-P4 and the flow through the SIE, EfficientNet, and the memory banks are not clearly labeled; Figure 4's axes labels are also ambiguous. Please provide a higher-resolution figure with explicit axis labels.
[Table 2] Table 2's 40 category names are concatenated into very wide rows, making it difficult to map results to categories; please reformat, for example by transposing the table or splitting it into two parts.

Circularity Check

1 steps flagged · score 2.0 of 10

Minor self-definitional step in the SIE information-capturing argument; the empirical SOTA evaluation is otherwise independent.

self definitional [Spatial Insight Engine, Equations (4)-(5)]
"After rewriting, we have: Iglobal = Itop + \sum_{i:zi≥zmid}(zi − zmid) ≥ Itop. (5) Therefore, we observe that Iglobal has more information than Itop, which is standard external projection manner."

I_global is defined in Eq. (4) as I_top plus a sum of non-negative terms max(0, z_i - z_mid). Eq. (5) then rewrites this definition and concludes I_global ≥ I_top. The 'more information' claim is therefore true by construction: adding non-negative numbers to I_top cannot decrease it. The step does not show that the added internal terms carry discriminative anomaly information. This is a definitional identity presented as theoretical support for the central motivation, though the later benchmark evaluation and ablations do not depend on this identity.

full rationale

The only circular step I can exhibit is the SIE information-capturing argument: the paper defines I_global as I_top plus non-negative terms, then concludes I_global contains more information than I_top. This is an arithmetic tautology, not an empirical or theoretical discovery, and it is used to motivate the internal-slice design. However, the paper's central SOTA claim rests on direct comparisons on the public Real3D-AD and Anomaly-ShapeNet benchmarks, and the ablation study independently evaluates the contribution of the internal slices. The use of Real3D-AD, introduced by co-authors of this paper, is a self-citation, but it is not load-bearing in the derivation: the benchmark is public, the competing baselines are external methods, and the reported numbers are empirical measurements rather than consequences of the cited work. The reported 0.757 vs. 0.767 O-AUROC inconsistency and the selection of feature-filtering hyperparameters using distributions from the same benchmark are correctness risks, but they are not circularity. Overall, the paper has one minor self-definitional motivation step while its experimental evaluation is self-contained; hence score 2.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The method's central pipeline relies on a small number of tuned scalars (alpha, beta, gamma) and one unreported radius, plus assumptions about registration, the meaning of 'information', and feature transferability. These are honest counts of what the paper pulls from data or assumes.

free parameters (4)

alpha (feature filter) = 0.2
Controls influence of the enhanced Laplacian term in Algorithm 1; selected by visual inspection of heatmaps on Real3D-AD (Figure 4), i.e., tuned on the target data.
beta (feature filter) = 0.2
Controls exponential decay rate of the similarity weight matrix in Algorithm 1; tuned on Real3D-AD via Figure 4.
gamma (feature filter) = 0.001
Controls contribution of the anomaly metric term in Algorithm 1; tuned on Real3D-AD via Figure 4.
patch radius r
Defines the neighborhood around each center point in Eq. (8); not reported anywhere, so the method cannot be reproduced exactly.

assumptions (4)

domain assumption All point clouds are registered by RANSAC so the z-axis has a consistent semantic orientation (top/bottom).
The SIE slicing at zmid (Eq. 2) and top/bottom projections depend on stable alignment; stated in Figure 2 caption (registration step), but no registration accuracy analysis is given.
ad hoc to paper Cumulative z-distance is a valid measure of the amount of anomaly-relevant information.
Eqs. (3)-(5) define 'information' as summed distances to zmax; this is an arbitrary proxy, and the inequality I_global >= I_top holds simply because non-negative terms are added.
ad hoc to paper Depth discrepancy between top-down and middle-up internal views correlates with anomalies.
Stated in Eq. (6) as a detection condition, but the implemented scoring uses nearest neighbors in memory banks; the equation is never linked to the actual pipeline.
domain assumption Pretrained PointMAE and EfficientNet features transfer to industrial 3D anomaly detection.
Features from pretrained models are used without task-specific fine-tuning; the paper provides no analysis of domain shift.

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Pith. "Pith review of Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection." pith.science (2026). https://pith.science/paper/4UHEAAAO

@misc{pith2026241213461,
  author       = {Pith},
  title        = {Pith review of: Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4UHEAAAO}},
  note         = {Machine review of arXiv:2412.13461}
}

read the original abstract

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception~(ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine~(SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 3.2\% and 13.1\%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks.

Figures

Figures reproduced from arXiv: 2412.13461 by the authors.

**Figure 2.** Overview of our method. We start by matching the point cloud according to RANSAC (Li, Hu, and Ai 2021). [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Visualization of projection slices. The images are [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Heatmaps of the impact of parameters α, β, γ on mean and variance. The ordinate represents a combination of α and β, and the abscissa represents γ. The lighter the color of the block in the figure, the larger the difference before and after the transformation. The red…

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Zeng, J.; Cheung, G.; Ng, M.; Pang, J.; and Yang, C. 2019. 3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model. arXiv:1803.07252

2019 arXiv
[44]

Zhao, H.; Jiang, L.; Jia, J.; Torr, P.; and Koltun, V. 2021. Point Transformer. arXiv:2012.09164

2021 arXiv
[45]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all...
[46]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Bastico, M.; Decencière, E.; Corté, L.; Tillier, Y.; and Ryckelynck, D. 2024. Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching. arXiv:2402.17372

work page arXiv 2024

[2] [2]

Bergmann, P.; Löwe, S.; Fauser, M.; Sattlegger, D.; and Steger, C. 2019. Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications

work page 2019

[3] [3]

Bergmann, P.; and Sattlegger, D. 2022. Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors. arXiv:2202.11660

work page Pith review arXiv 2022

[4] [4]

Bhunia, A.; Li, C.; and Bilen, H. 2024. Looking 3D: Anomaly Detection with 2D-3D Alignment. arXiv:2406.19393

work page Pith review arXiv 2024

[5] [5]

Cao, X.; Xia, H.; Han, X.; Wang, Y.; Li, K.; and Su, L. 2023. PointJEM: Self-supervised Point Cloud Understanding for Reducing Feature Redundancy via Joint Entropy Maximization. arXiv:2312.03339

work page Pith review arXiv 2023

[6] [6]

Cao, Y.; Xu, X.; and Shen, W. 2023. Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection. arXiv:2303.13194

arXiv 2023

[7] [7]

X.; Funkhouser, T.; Guibas, L.; Hanrahan, P.; Huang, Q.; Li, Z.; Savarese, S.; Savva, M.; Song, S.; Su, H.; Xiao, J.; Yi, L.; and Yu, F

Chang, A. X.; Funkhouser, T.; Guibas, L.; Hanrahan, P.; Huang, Q.; Li, Z.; Savarese, S.; Savva, M.; Song, S.; Su, H.; Xiao, J.; Yi, L.; and Yu, F. 2015. ShapeNet: An Information-Rich 3D Model Repository. arXiv:1512.03012

arXiv 2015

[8] [8]

C.; See, S.; and Li, H

Chen, X.; Shi, S.; Zhang, C.; Zhu, B.; Wang, Q.; Cheung, K. C.; See, S.; and Li, H. 2023. Trajectoryformer: 3d object tracking transformer with predictive trajectory hypotheses. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 18527--18536

work page 2023