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REVIEW 2 major objections 5 minor 35 references

REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining

T0 review · 2 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read REHEARSE-3D is a 9.2-billion-point multimodal dataset that labels raindrops point-wise in LiDAR-256 and 4D Radar clouds across day and night emulated rain.

desk verdict A valuable multimodal rain dataset whose benchmark numbers rest on a residual rain label definition that likely assigns false rain points in clean sequences. read the letter →

arxiv 2504.21699 v2 pith:5IBUEMHB submitted 2025-04-30 cs.CV cs.RO

Abu Mohammed Raisuddin , Jesper Holmblad , Hamed Haghighi , Yuri Poledna , Maikol Funk Drechsler , Valentina Donzella , Eren Erdal Aksoy This is my paper · ORCID
classification cs.CVcs.RO
keywords pointcloudde-rainingemulatedraindatasetLiDAR-2564DRadarsemanticannotationautonomousdrivingadverseweathersensornoisemodeling
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

REHEARSE-3D is a large multi-modal dataset built to support 3D point-cloud de-raining: detecting and removing raindrop returns in LiDAR and radar data before downstream perception. The paper's central claim is that this is the first dataset with point-wise semantic labels on high-resolution LiDAR-256 and 4D Radar point clouds across daytime and nighttime emulated rain, totaling 9.2 billion annotated points. The authors also benchmark five existing de-raining methods on the dataset and create a simulated rain version from clean scans to quantify the gap between physically simulated and sprinkler-emulated rain. If the dataset is sound, autonomous-driving research gets a high-density, multimodal, rain-characterized test bed in a domain where annotated adverse-weather data is scarce.

What carries the argument

The load-bearing object is the annotation protocol rather than a new network. It defines rain operationally: estimate the road plane with RANSAC, draw bounding boxes around known objects and sprinklers, draw a 2D polygon around the road, and treat every remaining point inside that polygon above the plane as a raindrop. This residual-class rule is what produces the 9.2 billion point-wise labels, and it is also the most delicate assumption in the paper. The secondary mechanism is the polar-grid-map preprocessing that reconstructs unreturned LiDAR beams, allowing an existing physics-based rain model to be applied to real clean scans to create the simulated benchmark.

What would settle it

Run the same annotation pipeline on the clean-weather REHEARSE-3D sequences and count how many points are labeled rain despite no rain being present; if the count is substantial, the residual-class rule contaminates the ground truth. A second check is to compare the labeled rain points against a physically validated raindrop detector on the same scenes.

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Extended reading notes

Core claim

REHEARSE-3D is a semantically annotated version of an existing controlled-weather dataset. It contains 143 sequences, each with roughly 300 dense LiDAR scans, captured with a high-resolution 256-line MEMS LiDAR and a 4D Radar at a fixed outdoor test track under sprinkler-generated rain at 10, 25, and 50 mm/h, in daytime and nighttime, plus clean conditions. Every point in the merged LiDAR-radar cloud is labeled as one of eight classes: rain, car, pedestrian, bike, sprinkler, targets, road, and background. The rain label is produced by the annotation rule that any point above the estimated road plane, inside the road polygon, and not assigned to a known object is a raindrop. The paper further contributes de-raining benchmark results for statistical filters and deep networks, and a simulated rain counterpart built by applying an established LiDAR rain model to clean scans, used to measure the emulated-to-simulated gap.

Load-bearing premise

The rain labels are valid only if every residual point above the road plane inside the road polygon is actually a raindrop; anything else there—sensor noise, dust, edge artifacts, or an unmodeled object—gets mislabeled as rain.

Editorial extensions

If this is right

  • Supervised models trained on these labels outperform unsupervised statistical filters by large margins (F1 around 97% versus roughly 34% at best on the test split), indicating that labeled rain data, not algorithm design, is the main bottleneck.
  • Because the dataset includes radar with transferred labels, fused LiDAR-radar de-raining can be benchmarked for the first time, opening a path to using weather-resilient radar to clean LiDAR.
  • Rain characteristics (intensity, droplet size distribution, wind, visibility) are provided per sequence, so point-level rain behavior can be tied to physical weather parameters rather than treated as generic noise.
  • The simulated-vs-emulated comparison shows a large domain gap (near-perfect scores on simulated rain, lower on emulated), so the dataset can serve as a calibration yardstick for rain simulators.
  • High-density LiDAR-256 labels reduce the sparsity problem that limited earlier 32- and 64-beam annotated weather datasets.

Reading between the lines

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

  • The residual-class rain definition means benchmark numbers measure detection of unexplained points in the road area. A control experiment running the same annotation on clean sequences would quantify how much sensor noise and dust are labeled as rain.
  • Radar labels are copied from the nearest LiDAR point. Because rain degrades LiDAR measurements, the radar rain labels may be spatially misaligned or missing in exactly the heavy-rain frames where radar would be most useful.
  • The reported gap between simulated and emulated rain can be used as a target for rain simulators: a simulator that closes that gap on this dataset would be a stronger candidate for transfer to real rain.
  • Because all scenes are static front-view captures, the dataset does not exercise motion artifacts or multi-frame temporal cues; de-raining models that exploit sequence information would need additional data.
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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

2 major / 5 minor

Summary. The paper introduces REHEARSE-3D, a multi-modal dataset of dense LiDAR-256 and 4D Radar point clouds captured in a controlled outdoor rain facility, with point-wise semantic annotations for eight classes. The dataset includes 143 sequences from the existing REHEARSE dataset, covering clean conditions and three emulated rain intensities (10, 25, and 50 mm/h), in daytime and nighttime. The authors also generate a simulated rain version by applying a physically based LiDAR rain model to clean point clouds. They benchmark several statistical filters (DROR, DSOR) and deep-learning models (3D-OutDet, SalsaNext, LiSnowNet-L1) on the task of detecting and removing rain points in early-fused LiDAR and Radar clouds, reporting precision, recall, F1, and mIoU. The central claims are that REHEARSE-3D is the largest point-wise annotated weather dataset, the first multi-modal dense LiDAR/4D Radar rain dataset, and a useful benchmark for point cloud de-raining.

Significance. If the point-wise rain labels are valid, REHEARSE-3D would be a valuable community resource: it is substantially larger and denser than existing annotated weather point-cloud datasets (e.g., WeatherNet, WADS, SemanticSpray), it is the first to combine 256-line MEMS LiDAR with 4D Radar point-wise labels under controlled rain, and it provides precipitation characteristics (intensity, droplet size distribution, wind, visibility) that are absent from most related datasets. The benchmark is useful and appears reproducible: the splits are described, the evaluation protocol is standard, and the authors plan to release the dataset and models. The use of a physically calibrated rain simulation applied to real clean scans is a methodological strength that enables an emulated-to-simulated domain-gap study. However, these contributions rest on the validity of the 'rain' label and of the radar label transfer, and the manuscript currently does not establish that validity rigorously.

major comments (2)
  1. [Section III-B, Step 6] The rain label is defined as a residual category: any point above the road plane, within the 2D road polygon, and not assigned to one of the hand-labeled classes is automatically labeled as a raindrop. The paper does not state that this rule is applied only to rainy sequences, and Figure 5 appears to show a non-negligible rain class under the 'clear' condition. Under this definition, sensor noise, dust, edge artifacts, and missed objects in the road area are all labeled as rain, so the ground-truth labels and the benchmark metrics in Tables II and III measure residual-point classification rather than rain detection. A model that flags every unassigned point as rain would obtain high scores by construction, not because it detects rain. This is load-bearing because the central contribution is point-wise rain annotation. Please (i) clarify precisely how Step 6 was applied to clean sequences, (ii) report the number and fraction of rain-labeled points in clean conditions, and (iii) validate a random sample of rain labels against manual/visual annotation or against the independently measured rain intensity maps, including regions with low measured intensity where false positives are most likely.
  2. [Section III-B, Step 10] 4D Radar points are annotated by transferring labels from the nearest LiDAR point, with no threshold or validation reported. Since LiDAR and 4D Radar have different sampling geometries, spatial resolutions, and occlusion patterns, nearest-neighbor transfer can assign object or rain labels to radar points that correspond to different physical scatterers. This is particularly relevant for the rain class, where radar returns may come from water droplets at different distances than the nearest LiDAR return. Please report the distribution of nearest-neighbor distances, validate a sample of radar labels manually, and either restrict label transfer to points within a physically motivated distance or provide an alternative annotation procedure for radar points.
minor comments (5)
  1. [Section III-C, Figure 5] The figure would benefit from a clearer legend and explicit per-condition class counts; in particular, it should be immediately clear whether the 'clear' condition contains any rain-labeled points and, if so, how many.
  2. [Section III-E, Algorithm 1] The polar-grid projection assigns each 3D point to the nearest calibrated elevation and azimuth index, but the pseudocode does not describe how multiple points mapping to the same grid cell are resolved, nor how invalid or duplicate cells are handled. Please add this detail, since it affects the unreturned-beam reconstruction and thus the simulated rain data.
  3. [Section IV-A] The text introduces Radius Outlier Removal and Statistical Outlier Removal as baselines but reports results only for DROR and DSOR. Either explain why ROR and SOR are omitted or remove them from the baseline description.
  4. [Section IV-B, Table III] The near-perfect scores of 3D-OutDet on the WMG simulated data are expected because the simulated rain is generated algorithmically from clean scans and the same algorithm is used to create the labels; the discussion should more explicitly warn readers that these numbers do not represent real-world detection performance and should not be used to calibrate expectations on physical rain data.
  5. [Section III-B, Step 2] The RANSAC road-plane estimation and the subsequent manual bounding-box correction are described briefly; please state how many points fall outside the estimated plane and how sensitive the rain-label count is to the RANSAC threshold, since Step 6 relies directly on this plane.

Circularity Check

1 steps flagged · score 6.0 of 10

Rain labels are residual unclassified points, so the de-raining benchmark's target is defined by the annotation rule itself.

  1. self definitional [Section III-B, Data Annotation, Step 6]
    "6) Any points above the road plane, within the 2D polygon bounding box, that do not belong to the previously labeled classes are then labeled as raindrops."

    The rain ground truth is the residual set of points above the road plane inside the road polygon that were not assigned to sprinkler, car, pedestrian, bike, or target classes. The benchmark then scores rain detection against these same labels. A model that outputs 'rain' for every point not inside a known object box matches the labels by construction, so outlier detectors such as 3D-OutDet are rewarded by the label rule rather than by physical rain measurement. Because Section III-B describes one pipeline for 'clean and rainy REHEARSE data' and Section III-C reports 39% clean sequences, the same residual rule would label sensor noise, edge artifacts, and missed objects as rain in clear weather, embedding the benchmark target into its own definition.

full rationale

The dataset's headline novelty (largest point-wise annotated multimodal rain dataset, LiDAR-256, 4D Radar, day/night controlled environment) is independent content and is not derived from the benchmark models. However, the central de-raining benchmark is partially circular: rain is not independently measured but is defined as whatever points remain unclassified in the road polygon. Consequently, the rain-detection scores in Tables II and III partly measure adherence to the annotation rule rather than physical raindrop detection. The simulated WMG data and near-100% 3D-OutDet results are also acknowledged by the authors as reflecting the simulation process, but this is a self-consistency check rather than a circular derivation, so I do not count it as a separate circular step. Overall, the dataset claim survives, but the validity of the benchmark ground truth is reduced by construction.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities, forces, or conserved quantities. The dataset itself is a new artifact, not a postulated entity, so the invented-entities ledger is empty. The free-parameter entry captures the tuned statistical filter hyperparameters that drive the unsupervised benchmark numbers, while the axioms capture the annotation conventions and simulation assumptions that the dataset's utility depends on.

free parameters (1)
  • DROR/DSOR statistical filter hyperparameters (radius and neighbor thresholds) = not reported; tuned via Optuna on 100 random samples for 100 iterations
    The de-raining benchmark results for the unsupervised baselines in Tables II and III depend on these tuned thresholds, but the final values and sensitivity analysis are not given.
assumptions (5)
  • ad hoc to paper Every point above the road plane inside the road polygon that is not assigned to another class is a raindrop.
    Section III-B step 6 defines the rain class as a residual category, assuming all unclassified points are physical raindrops rather than sensor noise, dust, or missed object points.
  • domain assumption Sprinkler-generated rain adequately emulates real rain for sensor-noise and de-raining research.
    Section III-A reports validation with disdrometers and Marshall-Palmer distributions, but the authors acknowledge in Section V that a gap between emulated and real rain remains.
  • ad hoc to paper Labels can be transferred from the nearest LiDAR point to annotate 4D radar points.
    Section III-B step 10 assumes spatial correspondence between LiDAR and radar points, which may mislabel radar returns due to differing sensor geometries and resolutions.
  • domain assumption The Espineira et al. LiDAR rain model, applied through the PGM pre-processing in Algorithm 1, faithfully simulates rain on real recorded point clouds.
    Section III-E relies on this prior model and on the reconstruction of unreturned beams via Polar Grid Maps; no independent validation of the simulated rain against the emulated rain is provided.
  • domain assumption The MEMS LiDAR calibration angles (azimuth and elevation) are accurately known and stable for reconstructing unreturned beams.
    Algorithm 1 depends on calibrated angle arrays to build Polar Grid Maps; calibration errors would corrupt the simulated rain points.

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Cite this review

Pith. "Pith review of REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining." pith.science (2026). https://pith.science/paper/5IBUEMHB

@misc{pith2026250421699,
  author       = {Pith},
  title        = {Pith review of: REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5IBUEMHB}},
  note         = {Machine review of arXiv:2504.21699}
}
read the original abstract

Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, the interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In this study, we release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining. Distinct from the most relevant competitors, our dataset is unique in several respects. First, it is the largest point-wise annotated dataset, and second, it is the only one with high-resolution LiDAR data (LiDAR-256) enriched with 4D Radar point clouds logged in both daytime and nighttime conditions in a controlled weather environment. Furthermore, REHEARSE-3D involves rain-characteristic information, which is of significant value not only for sensor noise modeling but also for analyzing the impact of weather at a point level. Leveraging REHEARSE-3D, we benchmark raindrop detection and removal in fused LiDAR and 4D Radar point clouds. Our comprehensive study further evaluates the performance of various statistical and deep-learning models. Upon publication, the dataset and benchmark models will be made publicly available at: https://sporsho.github.io/REHEARSE3D.

Figures

Figures reproduced from arXiv: 2504.21699 by the authors.

Figure 1
Figure 1. A sample scene from REHEARSE-3D. Fully annotated high-resolution LiDAR and fused 4D Radar point clouds are shown on the right. Each color represents a unique semantic class: rain, car, sprinkler, pedestrian, biker, road, targets, and background. For visualization purposes only, the corresponding RGB and thermal camera images are shown on the left. Abstract— Sensor degradation poses a significant challenge in autonom… view at source ↗
Figure 2
Figure 2. CARISSMA Outdoor test track (left) and RE [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Intensity heat map of the emulated rain at the [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Number of annotated points in each semantic class [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Annotated sample LiDAR point clouds (right) to [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: (a) Original emulated rainy scene, (b) Rain removed by SalsaNext [32], and (c) Rain removed by 3D [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.