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REVIEW 5 major objections 4 minor 103 references

SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation

T0 review · 5 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Submodular three-stage selection lifts LiDAR mIoU by up to 8 points

desk verdict A pragmatic three-stage heuristic for active LiDAR segmentation with real-looking gains, but the submodular framing is overstated and the class-balancing stage leans on the very predictions it is meant to fix. read the letter →

arxiv 2505.11516 v1 pith:NBJCS537 submitted 2025-05-06 cs.RO eess.IV

Ruiyu Mao , Sarthak Kumar Maharana , Xulong Tang , Yunhui Guo This is my paper · ORCID
classification cs.ROeess.IV
keywords activelearningLiDARsemanticsegmentationsubmodularoptimizationvoxel-centricselectionclassimbalanceMonteCarlodropoutpointcloudannotationlabelefficiency
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

The paper claims that active learning for LiDAR semantic segmentation can be made both scalable and class-aware by selecting annotation units at the voxel level rather than the point level. It proposes SELECT, a three-stage pipeline that first picks a diverse candidate set of voxels with a variance-based submodular score, then ranks them by Monte Carlo dropout uncertainty, and finally chooses the voxel whose predicted class distribution best balances the labeled set. On SemanticPOSS, SemanticKITTI, and nuScenes, the paper reports mIoU gains of +8.17, +5.06, and +5.05 percentage points over the previous state of the art under a 6,000-point labeling budget. If true, this means substantially fewer annotated LiDAR points are needed to reach a given segmentation accuracy in outdoor scenes.

What carries the argument

The central object is the three-stage voxel-centric selection pipeline. Stage 1 uses a variance-based submodular score on averaged point features with a concave gain function, maximizing a submodular objective via greedy selection to avoid pairwise distance computations. Stage 2 computes voxel-level uncertainty by averaging Monte Carlo dropout logits and taking the majority predicted class per voxel. Stage 3 estimates the class distribution of already-selected voxels and each candidate voxel, applies softmax normalization, and greedily selects voxels that maximize the entropy of the resulting class distribution. The diminishing-returns property of submodular functions is what lets the framework encourage diversity and balance while remaining scalable.

What would settle it

Run SELECT on SemanticPOSS with Stage 3's class proportions computed from ground-truth labels instead of model predictions; if the mIoU gain over the predicted-label version is small, the balancing stage is robust, while a large gap would show the stage is driven by the very misclassifications it is meant to fix.

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Extended reading notes

Core claim

The central claim is that a voxel-centric, unified submodular framework can jointly optimize informativeness, uncertainty, and class balance in active LiDAR semantic segmentation. SELECT works in three coordinated stages: it uses a variance-based submodular function to select a representative set of voxels without pairwise comparisons, aggregates Monte Carlo dropout logits to identify voxels where the model is uncertain, and then applies an entropy-based submodular maximization step to choose voxels that push the labeled point distribution toward class balance. The paper demonstrates this on three outdoor benchmarks with two backbones, and reports that the full three-stage pipeline outperforms all prior active learning baselines, with particularly strong per-class gains on rare classes such as pedestrians, riders, and cones.

Load-bearing premise

Stage 3 assumes the segmentation model's predicted labels are accurate enough to estimate the class distribution of both already-selected and candidate voxels, even for the rare classes the active learning loop exists to improve.

Editorial extensions

If this is right

  • Annotation budgets can be spent at voxel granularity, reducing human labeling effort while preserving a point-level budget control mechanism.
  • Rare classes such as pedestrian, rider, and cone receive enough selected points to reach nonzero mIoU where many baselines score near zero.
  • The feature-based submodular selection scales linearly with candidate voxels, avoiding clustering methods that require pairwise distance matrices.
  • Consistent gains across SemanticPOSS, SemanticKITTI, and nuScenes with two backbones suggest the pipeline transfers across sensor configurations and class taxonomies.

Reading between the lines

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

  • Because Stage 3 uses the model's predicted labels to estimate class proportions, a calibration failure on rare classes would bias the balancing step; a testable extension is to weight pseudo-labels by confidence or to compare with oracle class counts.
  • The paper uses voxel size 0.25 for selection and 0.05 for training, so the sensitivity of the full pipeline to selection-voxel granularity is a natural next experiment that could trade rare-class recall against computational cost.
  • The observation that SELECT selects about 9% multi-class voxels versus 5% for the prior baseline suggests that boundary and rare-class voxels are only a small fraction of the pool, implying a possible ceiling on gains achievable from uncertainty alone.
  • A direct test of the framework's generality would be to apply the same three stages to indoor LiDAR or dense point cloud segmentation, where voxel label homogeneity is weaker.
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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

5 major / 4 minor

Summary. The manuscript proposes SELECT, a three-stage active learning pipeline for LiDAR semantic segmentation. Stage 1 ('Voxel-Level Submodular Subset Selection') scores voxels by the variance of their averaged feature vectors and claims to select a diverse representative subset. Stage 2 ('Voxel-Level Model Uncertainty Estimation') ranks the surviving voxels using MC-dropout logits. Stage 3 ('Submodular Maximization for Point-Level Class Balancing') selects a final voxel per point cloud using an entropy criterion over predicted class proportions. The authors report consistent mIoU gains over a wide set of baselines on SemanticPOSS, SemanticKITTI, and nuScenes, with headline improvements of +8.17, +5.06, and +5.05 mIoU over Annotator, respectively, under a 6000-point budget. The central claim is that SELECT constitutes a unified submodular framework that is simultaneously scalable, uncertainty-aware, and class-balanced.

Significance. If the empirical results hold, the practical significance is real: reducing annotation cost in outdoor LiDAR semantic segmentation is an important problem, and the paper compares against many recent AL baselines on three standard benchmarks. The per-class tables and the stage ablations are useful evidence that rare-class performance can be improved by selection strategies. However, the significance is currently undercut by three issues: the 'submodular' Stage 1 objective is additive and reduces to variance ranking; Stage 3 with Lambda3=1 is a single entropy evaluation rather than a submodular maximization; and the class-balance stage depends on predicted labels in exactly the regime where the paper itself shows rare-class predictions are zero. The absence of error bars and code further weakens the support for the headline numerical claims. The manuscript would be a valuable practical contribution after these claims are corrected and the corresponding experiments are added, but in its present form the technical contribution is overstated.

major comments (5)
  1. [IV-A, Eqs. (3)-(5)] Stage 1 is not a submodular subset selection in any meaningful sense. The objective in Eq. (5) is f(S) = sum over V_j in S of g(sigma(f_{V_j})), so the marginal gain of adding a voxel is g(sigma(f_{V_j})), independent of the current set S; the function is additive, not merely submodular. Under the cardinality constraint the maximizer is simply the Lambda1 voxels with the largest sigma values, and no diversity, redundancy reduction, or pairwise interaction is enforced. Moreover, Eq. (4) computes the variance across the D components of the averaged voxel feature, not the spread of point-level features inside the voxel, so the text's claim that the score 'captures dissimilarity between point-level features' is not supported. The authors should either introduce an interaction term that actually encourages diversity or reframe Stage 1 as top-k variance selection and remove the submodularity claim from the paper's central contribution.
  2. [IV-C, Eqs. (8)-(10), V-A] With the experimental setting Lambda3=1, Stage 3 chooses a single voxel per point cloud per active-learning round. The greedy 'submodular maximization' is therefore a single evaluation of H(V_j) for each candidate, not an iterative optimization of a submodular function. The claimed contribution of submodular class balancing is not exercised by the reported protocol. If the authors wish to claim submodular maximization, they should either select multiple voxels per round at Stage 3 or explicitly clarify that submodularity is only a motivation for a one-step entropy heuristic.
  3. [IV-C, Eq. (8)] Stage 3 uses the model's predicted labels N_c^{V_j} to estimate the class composition of each candidate voxel. When the segmentation model is miscalibrated and assigns zero probability mass to a rare class, a voxel that actually contains that class contributes zero count to N_c^{V_j}, so the entropy objective cannot prefer it. This is not hypothetical: the paper's own ablation in Table III shows that Stages 1+2 alone produce 0 mIoU on the garbage-can and cone/stone classes on SemanticPOSS, and the full pipeline attributes +4.49 mIoU over Stages 1+2 to Stage 3. The manuscript provides no calibration analysis, no sensitivity test for the predicted-label dependency, and no error bars. The authors should add such an analysis or use a label-free uncertainty-aware class-balance criterion.
  4. [V-A] The annotation budget is specified as 6000 points, but the protocol states that every method selects Nq=1 voxel per point cloud per round. If all training scans are used, the number of selected voxels would be many thousands, and even a handful of voxels per scan would already exceed 6000 points; if only a subset of scans is used, the selection of that subset is not described. This makes the budget comparison and the reported point-level cost difficult to reproduce. Please specify the number of point clouds used per round, how the 6000-point budget is shared across point clouds, and how voxel point counts are counted.
  5. [V-B, Tables I-II] All reported mIoU values appear to be from a single run; no standard deviations, seeds, or code are provided. Given that the headline improvements are 5-8 mIoU points and several baselines differ by only 1-2 points, the claim of consistent superiority is not statistically supported. Please provide multiple seeds with error bars for at least the main tables and make code and configuration public.
minor comments (4)
  1. [Abstract] The abstract contains a typo: 'understating' should be 'understanding', and the comma in 'costly and, requires' should be removed.
  2. [III] The notation P_i = {V_j = {...}_{k=1}^{N_j}}_{j=1}^{N_i} in Section III is malformed and should be rewritten for clarity.
  3. [Tables I-II] Table II column headers contain OCR artifacts such as 'Ba.ier', 'Fraff.', and 'Sqn', and several class names are inconsistently capitalized; please proofread the tables.
  4. [IV-A, Eq. (4)] The text should state explicitly that the variance in Eq. (4) is over feature dimensions of the mean voxel feature, not over the point features within the voxel, to avoid the misleading wording in the surrounding paragraph.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the mIoU claims are measured against external benchmarks, and the self-referential selection signals are active-learning heuristics rather than predictions fitted to the evaluation quantity.

full rationale

SELECT's central claim is an empirical comparison: under a 6000-point labeling budget, the three-stage selector achieves higher mIoU than prior active-learning methods on SemanticPOSS, SemanticKITTI, and nuScenes. These numbers come from held-out ground-truth evaluation, not from a parameter fitted to those datasets and then renamed as a prediction. No equation in the paper defines the reported mIoU in terms of the selection scores, so no 'prediction' reduces by construction to an input. The self-referential elements are real but not circular: Stage 2 uses the model's own MC-dropout predictions to estimate uncertainty, and Stage 3 (Eqs. 8-10) uses predicted class counts to estimate candidate-voxel class proportions. This is the standard active-learning loop, and the evaluation is still against ground-truth labels. The paper's own ablation (Table III) exposes the consequence of this dependency, showing that Stages 1+2 alone give 0 mIoU on garbage-can and cone/stone on SemanticPOSS; that is a robustness limitation about a heuristic, not a logical reduction of the central claim to its inputs. Stage 1's objective (Eq. 5) is additive over per-voxel variance scores, so with increasing g the selection is effectively a top-Λ1 variance ranking; the 'submodular diversity' wording is stronger than what the equation guarantees, but this overclaim concerns the method's framing, not circularity of the experimental result. Self-citations [74] and [100] appear only in related-work lists, and no load-bearing argument cites the authors' prior work. Therefore, the correct finding is no significant circularity; the empirical claims stand or fall on the reported experiments, not on a self-citation chain or on equations that are equivalent to their own inputs.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The method introduces no new physical or mathematical entities. It relies on hand-set hyperparameters (voxel size and stage sizes) and several domain assumptions about voxel label homogeneity, logit calibration, and the reliability of predicted labels for class balancing. The most consequential assumption is that predicted labels can be used to balance classes, since that creates a feedback loop that can reinforce model bias.

free parameters (6)
  • Voxel size lambda for selection = 0.25
    Chosen by hand after ablation on SemanticPOSS; affects the number and content of voxels and is fixed across all datasets.
  • Voxel size lambda for training = 0.05
    Chosen for training granularity; the paper reports 0.25 was selected as a compromise between performance and training time.
  • Lambda1 (voxels kept after Stage 1) = 200
    Hand-set number of candidate voxels after feature-variance ranking; no sensitivity analysis is reported for this parameter.
  • Lambda2 (voxels kept after Stage 2) = 5
    Hand-set number of uncertain voxels passed to the balancing stage; fixed for all datasets without ablation.
  • Lambda3 (voxels finally selected) = 1
    One voxel per point cloud per query round; this makes the Stage 3 submodular maximization a single entropy evaluation.
  • MC-dropout forward passes T
    The number of Monte Carlo samples per point is never specified, though it directly determines the voxel uncertainty estimate.
assumptions (5)
  • standard math The additive objective F(S)=sum over selected voxels of g(sigma(f_Vj)) is submodular, so greedy maximization yields a diverse, representative subset.
    True that additive functions are submodular, but the diminishing-returns property is vacuous: marginal gains are constant, so no diversity or redundancy reduction is enforced. The paper invokes this axiom in Section IV Stage 1 to claim non-redundant selection.
  • domain assumption Points within the same voxel typically share the same semantic label in outdoor LiDAR scans.
    Used in Stage 2 to aggregate point-level predictions into a voxel-level label, supported by Fig. 3. However, the method targets boundary voxels where this assumption fails, so the majority-label aggregation may be unreliable precisely where it matters.
  • domain assumption A low average logit of the majority class within a voxel indicates low model confidence and high informativeness.
    Ad hoc assumption stated before Eq. (7); logit magnitudes are not calibrated probabilities and can vary across classes and scenes, so the uncertainty score is not a principled measure.
  • domain assumption The current model's predicted labels can be used to estimate class distribution for balancing the annotated set.
    Stage 3 counts predicted class labels in Eqs. (8)-(10). If the model is biased on rare classes, the balancing step amplifies that bias instead of correcting it.
  • standard math Maximizing the entropy of class proportions improves rare-class performance.
    Entropy is a standard diversity measure, but the greedy selection with Lambda3=1 means only one voxel is chosen, so the objective is evaluated once rather than optimized over a set.

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Cite this review

Pith. "Pith review of SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation." pith.science (2026). https://pith.science/paper/NBJCS537

@misc{pith2026250511516,
  author       = {Pith},
  title        = {Pith review of: SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NBJCS537}},
  note         = {Machine review of arXiv:2505.11516}
}
read the original abstract

LiDAR-based semantic segmentation plays a vital role in autonomous driving by enabling detailed understanding of 3D environments. However, annotating LiDAR point clouds is extremely costly and requires assigning semantic labels to millions of points with complex geometric structures. Active Learning (AL) has emerged as a promising approach to reduce labeling costs by querying only the most informative samples. Yet, existing AL methods face critical challenges when applied to large-scale 3D data: outdoor scenes contain an overwhelming number of points and suffer from severe class imbalance, where rare classes have far fewer points than dominant classes. To address these issues, we propose SELECT, a voxel-centric submodular approach tailored for active LiDAR semantic segmentation. Our method targets both scalability problems and class imbalance through three coordinated stages. First, we perform Voxel-Level Submodular Subset Selection, which efficiently identifies representative voxels without pairwise comparisons, ensuring scalability. Second, we estimate Voxel-Level Model Uncertainty using Monte Carlo dropout, aggregating point-wise uncertainties to identify informative voxels. Finally, we introduce Submodular Maximization for Point-Level Class Balancing, which selects a subset of points that enhances label diversity, explicitly mitigating class imbalance. Experiments on SemanticPOSS, SemanticKITTI, and nuScenes benchmarks demonstrate that SELECT achieves superior performance compared to prior active learning approaches for 3D semantic segmentation.

Figures

Figures reproduced from arXiv: 2505.11516 by the authors.

Figure 1
Figure 1. The proposed SELECT employs a unified submodular [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Left: The active learning pipeline for LiDAR semantic segmentation. Right: The proposed SELECT, which consists of three key stages — efficiently selecting points that are both informative and well-balanced in label distribution for training the LiDAR semantic segmentation model. hierarchical framework that incrementally filters out uninfor￾mative voxels ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. We summarize the statistics of commonly used datasets [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: The mIoU results of AL baselines and SELECT across each active learning round for the SemanticPOSS, SemanticKITTI, [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Visualization of inference results on the SemanticPOSS dataset using MinkNet. Our method, SELECT, accurately [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: The plot shows that the proposed SELECT selects more [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

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