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REVIEW 3 major objections 6 minor 63 references

CHaRM: Conditioned Heatmap Regression Methodology for Accurate and Fast Dental Landmark Localization

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read CHaRNet detects dental landmarks directly on 3D intraoral scan point clouds, reaching 0.56 mm mean error on standard models and avoiding tooth segmentation entirely.

desk verdict Solid dental-landmarks paper with a real new dataset, but the headline CHaR module is never ablated from the multi-task head and loss, so the central attribution is under-evidenced. read the letter →

arxiv 2501.13073 v5 pith:SAT2BF6X submitted 2025-01-22 cs.CV

José Rodríguez-Ortega (1 , 2) , Francisco Pérez-Hernández (1) , Siham Tabik (2) ((1) Nemotec , Madrid , Spain , (2) Department of Computer Science , Artificial Intelligence
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University of Granada Granada Spain)
This is my paper · ORCID
classification cs.CV
keywords dentallandmarkdetectionintraoralscanspointclouddeeplearningheatmapregressionconditionedtoothpresenceclassificationorthodontictreatmentplanningIOSLandmarks-1k
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The pith

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

The reading

The paper proposes CHaRM, a fully end-to-end deep learning methodology that localizes five dental landmarks per tooth directly on intraoral scan point clouds, eliminating the tooth-segmentation stage that previous methods require. The central idea is to condition the heatmap regression on predicted per-tooth presence probabilities, so landmarks of missing teeth are routed to a dedicated null point instead of hallucinating positions in the mesh. Evaluated on a new annotated dataset of 1,214 dental models, the best instantiation, CHaRNet, reports a mean Euclidean distance error of 0.56 mm on standard dentitions and 1.12 mm macro-averaged across all dentition types, while running up to 14.8x faster on GPU than the two-stage TSMDL baseline. A sympathetic reader would care because this removes a costly, error-prone preprocessing step and handles missing teeth in a single network.

What carries the argument

The load-bearing component is the Conditioned Heatmap Regression (CHaR) module, applied after the decoder and heatmap head. It takes the initial heatmap for landmark (t, g), the predicted presence probability p_t of tooth t, and a special null point placed outside the point cloud, then computes final scores as real-point likelihoods weighted by p_t and the null-point likelihood weighted by 1 - p_t. This switch-like weighting lets the network dynamically relocate landmarks of absent teeth to the null point while keeping landmarks of present teeth on the mesh, and it is trained jointly with a binary cross-entropy presence-classification head and an MSE heatmap regression loss.

What would settle it

Recompute MEDE and MSR over all existing landmarks, treating false negatives (landmarks predicted at the null point that actually exist) as errors at least as large as the largest observed distance, and re-rank CHaRNet against TSMDL and ALIIOS; if CHaRNet no longer leads, the reported accuracy gain is an artifact of the true-positive-only protocol.

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Extended reading notes

Core claim

The paper claims that a simple multiplicative gating of heatmap scores by tooth-presence probabilities turns a generic point cloud encoder-decoder into a clinically useful dental landmark detector that works on incomplete dentitions. For each landmark associated with tooth t, the CHaR module multiplies the likelihoods of all real mesh points by the predicted presence probability p_t and multiplies the null-point likelihood by (1 - p_t), so the final heatmap maximum lands either on the tooth or on a fixed external placeholder. With PointMLP as the encoder, the resulting CHaRNet beats the segmentation-based TSMDL and ALIIOS pipelines on nearly every dentition subtype in localization error and success rate, and does so without segmenting teeth at inference time.

Load-bearing premise

The evaluation protocol measures localization accuracy only on landmarks the model correctly identified as present, so a model that misses difficult landmarks entirely can look more accurate than one that detects them with large errors.

Editorial extensions

If this is right

  • Orthodontic workflows can obtain per-tooth landmarks without a separate tooth-segmentation stage, removing segmentation error propagation.
  • CHaRNet's GPU inference time of about 0.24 seconds per model makes real-time landmark placement feasible in treatment-planning software.
  • The CHaR mechanism improves five different point cloud backbones, so the conditioning idea transfers beyond a single architecture.
  • The public release of 1,214 annotated models gives the field a benchmark that includes missing teeth and third molars, which prior datasets largely lacked.

Reading between the lines

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

  • Because the reported MEDE and MSR are computed only over true-positive landmarks, a model that silently misses hard landmarks can appear more accurate than one that finds them with large errors; re-ranking the methods after counting false negatives as large errors would test how much of the advantage survives.
  • The same conditioning mechanism could transfer to other landmark-detection tasks on point clouds where individual parts may be absent, such as craniofacial or orthopedic keypoints, provided a null-point convention can be defined.
  • A direct ablation that feeds ground-truth presence labels instead of predicted probabilities into the CHaR module would isolate how much of the gain comes from the weighting rule versus the quality of the presence classifier.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. CHaRM is an end-to-end methodology for localizing five anatomical landmarks per tooth on 3D intraoral scans. The method uses a point-cloud encoder/decoder with a heatmap regression head, a teeth-presence classification head, and a CHaR module that multiplies each landmark's heatmap by the predicted presence probability of its tooth and weights an added null point by the absence probability. A new dataset, IOSLandmarks-1k (1,214 models), with a ten-class dentition taxonomy, is introduced. Experiments compare five backbones with and without CHaR and compare the best variant (CHaRNet, based on PointMLP) with TSMDL and ALIIOS. The paper reports a MEDE of 0.56 mm on standard dentitions and 1.12 mm macro-averaged across dentition types, MSR of 85.2% and 68.5%, and a 14.8x GPU speedup over TSMDL.

Significance. The dataset and the promised public release are valuable contributions to a field with scarce open data, and the efficiency gain from avoiding a segmentation stage is practically meaningful. The conditioning idea is simple and plausible, and evaluating five backbones gives evidence of general applicability. However, the central causal claim about the CHaR module is not yet empirically supported because the CHaR versions differ from the base models by more than the conditioning operation, and the evaluation metrics are true-positive-conditional with no uncertainty estimates. If the missing ablation and full-set metrics confirm the reported trends, CHaRNet would be a solid state-of-the-art result; as it stands, the strength of the conclusions exceeds the evidence.

major comments (3)
  1. [§4.2, §5.4, Eqs. (5)–(6)] The central claim that the CHaR module drives the improvements is not identified by the experimental design. In Tables 5–7, the 'Base' condition is trained only with the MSE loss (Eq. 3), while the 'CHaR' condition simultaneously adds the classification head, the BCE loss with λ_cls=1 and λ_reg=0.001 (Eq. 5), and the multiplicative weighting in Eq. (6). The gains could therefore be caused by multi-task regularization or by the extra capacity of the classification branch alone, rather than by the conditioning operation. An ablation that keeps the classification head and BCE loss but replaces Eq. (6) with an identity (or with ground-truth presence labels) is needed to attribute the improvement to the proposed module; without it, the paper's main novelty claim is unsupported.
  2. [§5.3, Eqs. (8)–(9)] MEDE and MSR are computed only over true-positive landmarks, so landmarks that are missed (false negatives) never enter the localization error. Consequently, the reported MEDE of 0.56 mm for CHaRNet is a conditional quantity: it measures accuracy only on the subset of landmarks that the model detected. A model that detects fewer hard landmarks can look more accurate under this protocol. Reporting F1 alongside mitigates this only partially, and CHaRNet's higher F1 in the SOTA comparison makes its TP-conditioned errors relatively harder, but the headline numbers in the abstract are still not full-set localization errors. I recommend also reporting an error measure that penalizes false negatives (e.g., assigning FN a fixed distance penalty or computing MEDE over all landmarks of present teeth), and reporting confidence intervals or multiple-seed statistics, since all tables present single-split point estimates.
  3. [§5.4, Tables 6–7] The Base-versus-CHaR localization comparison uses different rules for defining true positives. For base models, a tooth is considered absent if any of its five landmarks is predicted at the null point, which removes all five landmarks from MEDE/MSR; for CHaR versions, presence comes from the classification head and each landmark is evaluated independently. This asymmetry can distort Tables 6–7, because one misplaced landmark in a base model eliminates the other four (possibly accurate) landmarks from the localization statistics. The comparison should be repeated under a common TP rule, or with full-set metrics that do not depend on the heuristic presence criterion.
minor comments (6)
  1. [§1] The text contains the typo 'CHarNet' in the introduction; it should be 'CHaRNet'.
  2. [§5.5] The text says CHaRNet reduces the micro-averaged MEDE to 0.80 mm, but Table 9 reports a micro-avg MEDE of 0.70 mm for CHaRNet; please reconcile the discrepancy.
  3. [§6] The discussion states that CHaR improves 'the encoding of local geometric information,' but CHaR reweights heatmap values rather than modifying the encoder; this sentence mischaracterizes the method and should be rephrased.
  4. [§1 and §2.2] The claim of being 'the first fully end-to-end deep learning approach' should be qualified, since c-SCN [7] is described in Section 2.2 as an end-to-end method for tooth segmentation and landmark localization, albeit on crowns only.
  5. [§4.2, Eq. (6)] The set-union notation in Eq. (6) is ambiguous for what should be an ordered heatmap vector; define the update by index (e.g., the final heatmap entries) so that the argmax in Eq. (2) is well-defined.
  6. [§5.2] The sentence describing training of CHaR-based networks with 'the MSE loss (Formula 3) and the combined loss (Formula 5), respectively' is ambiguous; clarify that base versions use Eq. (3) and CHaR versions use Eq. (5).

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: CHaRM is an empirical supervised pipeline evaluated on a held-out split, and the CHaR conditioning is not defined in terms of the reported metrics.

full rationale

This paper is an empirical deep learning contribution, not a mathematical derivation chain. The CHaR module (Eq. 6) multiplies initial heatmap likelihoods by tooth-presence probabilities from a separate classification head, both trained with ground-truth labels (Eqs. 3-5) derived from the dataset annotations. No parameter is fitted to the test set: the patient-grouped, stratified holdout split (Section 5.1) means the reported MEDE, MSR, and F1 values are held-out predictions, not fitted quantities renamed as predictions. The TP-only localization metrics (Eqs. 8-9) exclude false negatives, but F1 is reported separately in Table 8 and the same protocol is applied to all compared methods, so this is a metric design choice rather than a circular reduction. The paper does not rely on load-bearing self-citations: TSMDL and ALIIOS are external methods with disjoint author lists, and no uniqueness theorem is imported from the authors' prior work. The main genuine weakness is that the gain of the CHaR conditioning is not isolated from the added classification head and BCE loss, so the contribution of Eq. 6 alone is not empirically pinned down; however, that is an attribution/ablation confound, not a case where an output is equivalent to an input by construction. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The method rests on a handful of design choices (σ, λ weights, null point placement) that are set by hand, plus domain assumptions about annotation quality and the clinical meaning of the 1 mm threshold. None of these are fitted to the test set, and the paper does not claim a derivation, so the axiom ledger is modest.

free parameters (5)
  • σ (Gaussian heatmap standard deviation) = 2 mm
    Set by hand in Section 3.4; controls the spatial spread of the landmark heatmap targets.
  • λ_reg, λ_cls (loss weights) = 0.001 and 1
    Chosen in Section 5.2 to balance the larger scale of the MSE loss against the BCE loss; no sensitivity analysis shown.
  • Downsampled point count = 10,000 (plus null point = 10,001)
    Chosen as a resolution/compute trade-off in Section 3.3.
  • Null point offset = c + m_b/2 · (0,1,0)
    Data-dependent placement rule in Section 3.3; arbitrary 1D offset, no ablation.
  • MSR threshold r = 1 mm
    Clinically chosen threshold in Eq. 9; no alternative thresholds explored.
assumptions (5)
  • domain assumption Expert landmark annotations are accurate and consistent
    The ground truth for all training and evaluation is taken as correct; Section 3 states models are 'annotated with detailed reference landmarks'.
  • domain assumption Dental models can be centered and oriented along the z-axis
    Section 3.1 says models are centered on the origin and oriented parallel to the z axis, which is required for the null point placement and consistent input.
  • domain assumption Gaussian heatmaps with σ=2mm are a valid target representation
    Section 3.4 defines the heatmap target; the choice of σ affects the learning problem.
  • domain assumption The null point is a sensible placeholder for absent landmarks
    Section 3.3 introduces a synthetic point outside the cloud; this assumes that landmarks of missing teeth should be assigned to a single external point rather than being undefined.
  • standard math Standard deep learning training assumptions (i.i.d. data, no leakage)
    Section 5.1 describes a stratified patient-level split to prevent leakage.
invented entities (1)
  • Null point
    purpose: A single extra point added outside the point cloud to serve as the target/placeholder for landmarks of missing teeth, allowing the heatmap argmax to output 'absent'.
    This is a modeling construct, not an empirical entity. It is central to the CHaR scheme (Section 3.3, Figure 5) but has no external falsifiable predictions.

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Cite this review

Pith. "Pith review of CHaRM: Conditioned Heatmap Regression Methodology for Accurate and Fast Dental Landmark Localization." pith.science (2026). https://pith.science/paper/SAT2BF6X

@misc{pith2026250113073,
  author       = {Pith},
  title        = {Pith review of: CHaRM: Conditioned Heatmap Regression Methodology for Accurate and Fast Dental Landmark Localization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SAT2BF6X}},
  note         = {Machine review of arXiv:2501.13073}
}
read the original abstract

Identifying anatomical landmarks in 3D dental models is essential for orthodontic treatment, yet manual placement is labor-intensive and requires expert knowledge. While machine learning methods have been proposed for automatic landmark detection in 3D Intraoral Scans (IOS), none provide a fully end-to-end solution that avoids costly tooth segmentation. We present CHaRM (Conditioned Heatmap Regression Methodology), the first fully end-to-end deep learning approach for tooth landmark detection in 3D IOS. CHaRM integrates four components: a point cloud encoder, a decoder with a heatmap regression head, a teeth-presence classification head, and the novel CHaR module. The CHaR module leverages teeth-presence information to adapt to missing teeth, improving detection accuracy in complex dental cases. Unlike two-stage workflows that segment teeth before landmarking, CHaRM operates directly on IOS point clouds, reducing complexity, avoiding error propagation, and lowering computational cost. We evaluated CHaRM with five point cloud learning backbones on IOSLandmarks-1k, a new dataset of 1,214 annotated 3D dental models. Both the dataset and code will be publicly released to address the scarcity of open data in orthodontics and foster reproducible research. CHaRM with PointMLP, named CHaRNet, achieved the best accuracy and efficiency. Compared to state-of-the-art methods (TSMDL and ALIIOS), CHaRNet reduced mean Euclidean distance error to 0.56 mm on standard dental models and 1.12 mm across all dentition type, while delivering up to 14.8x faster inference on GPU. This end-to-end approach streamlines orthodontic workflows, enhances the precision of 3D IOS analysis, and enables efficient computer-assisted treatment planning.

Figures

Figures reproduced from arXiv: 2501.13073 by the authors.

Figure 1
Figure 1. Four views of a complete dentition (16 teeth) with its corresponding 80 landmarks (5 per tooth). [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Examples of each dentition type based on our proposed taxonomy. The rows represent the presence or absence [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Count of each tooth in the dataset. The green dashed line represents the total number of dental models, i.e., [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Original (top row) and downsampled (bottom row) point clouds. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: 4 views of an input sample: A point cloud with 10000 points randomly subsampled from a 3D dental model [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Pipeline of CHaRM. Given an input point cloud of a 3D dental model, (1) the point cloud encoder extracts [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Overview of the Conditioned Heatmap Regression (CHaR) module. Initial heatmaps are adjusted by weighting [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Qualitative comparison between base networks (top row), CHaR-based networks (middle row) and ground [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Qualitative comparison between ALIIOS, TSMDL and CHaRNet on a standard dentition (a) and on a complex [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.