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REVIEW 3 major objections 5 minor 32 references

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read DenoiseCP-Net filters simulated rain, snow, and fog from LiDAR voxel grids before sharing, keeping detection accuracy unchanged while cutting bandwidth by up to 23.6%.

desk verdict A clean multi-task backbone for collective perception that delivers real bandwidth savings in simulation, but the closed-loop weather evaluation makes external validity the load-bearing question. read the letter →

arxiv 2507.06976 v1 pith:2CXXYRHK submitted 2025-07-09 cs.CV

Sven Teufel , Dominique Mayer , Jörg Gamerdinger , Oliver Bringmann This is my paper
classification cs.CV
keywords collectiveperceptionLiDARadverseweather3Dobjectdetectionpointclouddenoisingsparsevoxelgridsmulti-tasklearningbandwidthreduction
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 tries to establish that adverse-weather noise can be stripped out of LiDAR-based collective perception without paying a detection penalty, and that doing so before transmission makes the whole system cheaper. The authors propose DenoiseCP-Net, a single sparse-convolution network that labels each occupied voxel of the ego vehicle's LiDAR grid as noise or structure and, at the same time, detects 3D objects from fused local and cooperative voxel grids. To study the problem they extend OPV2V with simulated rain, snow, and fog and show near-perfect voxel denoising (F1 above roughly 96% in dense fog and above 99% in other conditions), detection accuracy essentially unchanged compared with sharing noisy voxels, an average bandwidth reduction up to 23.6%, and reduced inference latency in cooperative vehicles. The practical payoff would be collective perception that stays accurate in bad weather and uses less communication and compute; the main condition is that the simulated weather noise resembles real sensor noise.

What carries the argument

The central object is the shared sparse 3D convolution backbone with a U-Net-style denoising decoder and a parallel collective fusion backbone connected by a scatter operation. The shared backbone processes the ego vehicle's noisy voxel grid; its bottleneck features feed both the denoising decoder, which labels every occupied voxel noise or no-noise, and, through element-wise maximum scatter fusion, the collective fusion backbone, which ingests cooperative voxel grids from other vehicles. After training, voxels predicted as noise are deleted before the ego grid is shared, so the information transmitted is a denoised sparse voxel grid with coordinates only. This joint design is what avoids the redundant computation of a separate denoiser-plus-detector pipeline and what makes the bandwidth and latency reductions possible.

What would settle it

Train DenoiseCP-Net on the simulated weather-augmented OPV2V data and evaluate it on real rainy, snowy, or foggy LiDAR sequences with physically validated noise labels; if per-voxel noise F1 drops substantially below the simulated near-99% levels, or if detection AP with denoised sharing falls below AP with noisy sharing, the central claim fails. A simpler check is to compute the transmitted payload size for real adverse-weather point clouds before and after denoising; if real noise does not inflate the voxel count, the reported 23.6% bandwidth reduction will not appear.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that voxel-level weather-noise classification and 3D object detection can be done by one shared sparse-convolution backbone, and that the resulting denoised sparse voxel grid is exactly as useful to a cooperative detector as the noisy grid. On the weather-augmented OPV2V test split, DenoiseCP-Net reaches per-voxel noise accuracy of 99.73% (snow), 99.77% (rain), 99.98% (light fog), and 99.87% (dense fog), with F1 scores near the same levels. Detection AP with denoised cooperative data is essentially identical to AP with noisy data in every weather condition, for example 61.29 vs 61.29 in snow and 80.86 vs 80.82 in rain, and mixed fleets of half-denoised, half-noisy vehicles stay within a point or two. Because noise voxels are removed before transmission, the average bandwidth needed to share a vehicle's grid at 10 Hz falls by up to 23.6% (snow), and under high-intensity fog and snow it falls by 52.3% and 62.3%. The inference latency of the whole system also drops, most strongly in snow, by 6.2%.

Load-bearing premise

The load-bearing premise is that the simulated rain, snow, and fog point clouds from the authors' prior weather models, and the voxel labels they generate, are close enough to real-world LiDAR weather noise that a network trained on them will denoise real deployments equally well.

Editorial extensions

If this is right

  • A cooperative vehicle can clean its own LiDAR data and detect objects in one forward pass, eliminating a separate denoising network and its redundant computation.
  • Weather noise carries no information needed by the downstream detector, so filtering it before sharing does not hurt AP in rain, snow, light fog, or dense fog.
  • The bandwidth cost of collective perception in adverse weather can be cut by up to 23.6% on average and by over half in extreme fog and snow, while inference latency also falls.
  • Mixed deployments, in which only some vehicles denoise before sharing, retain nearly the same detection accuracy as fully noisy or fully denoised fleets.
  • The same joint architecture can be applied to other dense voxel tasks such as semantic segmentation, since denoising is structurally a per-voxel segmentation problem.

Reading between the lines

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

  • A reader can infer that weather-adaptive sharing is possible: vehicles could decide to denoise and transmit only when the local voxel grid contains a large fraction of predicted noise, using the denoising confidence as a control signal.
  • The scatter-max fusion assumes all agents use the same backbone; a heterogeneous fleet of vehicles with different feature extractors would need a feature-space alignment mechanism before the reported gains transfer.
  • The same joint denoise-and-detect pattern should carry over to other adverse-weather perception modalities, such as camera BEV or radar voxel grids, where noise statistics differ and the bandwidth-latency trade-off may shift.
  • One testable extension is to run the trained model on real rainy or snowy LiDAR sequences and track per-voxel F1 and transmitted payload size; those numbers would show how much of the simulated gain survives real noise.
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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 / 5 minor

Summary. The paper proposes DenoiseCP-Net, a multi-task sparse-voxel architecture for LiDAR-based collective perception that jointly performs voxel-level noise filtering and 3D object detection with a shared sparse-convolution backbone. The authors extend the OPV2V dataset by simulating rain, snow, light fog, and dense fog using their previously developed weather simulation models, and evaluate DenoiseCP-Net under three cooperative-sharing schemes: noisy, denoised, and mixed. Reported results include near-perfect per-class denoising accuracy and F1 scores (Table II), 3D object detection AP that is nearly unchanged when noisy cooperative voxel grids are replaced by denoised ones (Tables III–IV), bandwidth reductions of up to 23.6% (and up to 62.3% under high-intensity weather, Table V), and modest inference-latency reductions. The paper concludes that removing weather-induced noise before communication reduces bandwidth and computation without sacrificing detection performance.

Significance. The engineering idea of coupling voxel-level denoising with detection in one sparse-convolution backbone is sensible and potentially useful: it avoids a separate denoising stage, and the reported AP preservation under denoised transmission is internally consistent within the simulated domain. The paper also provides clearly described, physically motivated weather simulation models (drop-size and snowflake-size distributions, fog chamber fitting) and reports per-class metrics, which is more informative than a single aggregate score. The main limitation is external validity: all quantitative claims are produced on the authors' own simulated weather, with denoising ground truth generated by the same simulation, so the headline numbers should be read as simulation-domain results unless additional real-data evidence is supplied. If the claims are appropriately scoped, this would be a solid contribution to the collective-perception literature, but the current manuscript overstates the strength of the evidence.

major comments (3)
  1. [VI-A (with IV-A–C)]
  2. [V-A / VII-C]
  3. [Table III]
minor comments (5)
  1. [VI-C / Table II]
  2. [VI-B / VII-C]
  3. [VII-A]
  4. [VII-B]
  5. [II-B / Contributions]

Circularity Check

2 steps flagged · score 6.0 of 10

Adverse-weather denoising and bandwidth claims are evaluated inside a closed loop: the simulator defines both the noise and the ground-truth labels, so near-perfect F1 and bandwidth savings measure the authors' synthetic weather, not independently validated real-weather behavior.

  1. self definitional [Section VI-A (Datasets, voxel-level ground truth) and Section VII-A (Denoising results)]
    "The false points caused from the rain, snow, or fog simulation are then labeled noise, while all other points are labeled no noise. During voxelization, these labels are propagated to voxels by majority vote to provide the voxel-level ground truth."

    The denoising target is defined by the same simulation (Section IV, using the authors' prior work [5]) that generates the noisy input. Thus the near-perfect F1 scores in Table II measure how well the network reproduces the simulator's own false-point labeling, and the bandwidth reductions in Tables III and V are essentially the fraction of voxels the simulator injected as noise. The headline claim of near-perfect denoising 'in adverse weather' is therefore an evaluation of self-consistency with the authors' synthetic weather generator, not an independent measurement against real rain, snow, or fog LiDAR noise. The result is true by the paper's operational definition of noise, not by external evidence.

  2. self citation load bearing [Section IV (Weather Simulation) and Section VII-B (Object Detection with denoised data)]
    "Since there is no real-world dateset for collective perception in adverse weather available, we use realistic weather simulation models for LiDAR point clouds in order to evaluate collective perception in the desired weather conditions. As simulation models we use the rain, snow and fog simulation for LiDAR point clouds from our previous work [5]."

    The realism of every weather-specific conclusion is imported from a self-citation ([5]) that is not independently verified in this paper: rain and snow statistics are not quantitatively compared with real LiDAR weather noise, and the simulation parameters are selected by the authors (Table I). The subsequent claim that 'the removed noise does not contain any information that is valuable for the object detection task' is forced by construction, because the simulation places false points in free space; the near-identical noisy/denoised AP (Table III) therefore follows from how the test data were generated rather than from a discovered property of real adverse weather.

full rationale

The derivation chain for the weather-specific claims is closed: the simulator from the authors' prior work generates the noise, the same simulator's output defines the ground-truth labels, and the network is trained and evaluated on those labels. Consequently the headline numbers (99%+ F1, up to 23.6% bandwidth reduction, AP parity after denoising) quantify agreement with, and properties of, the authors' own simulation rather than independently established real-weather behavior. This is not a formal logical fallacy in the architecture derivation: the multi-task sparse-convolution design is a genuine contribution, and the clear-weather OPV2V detection result (AP 83.05) is an external benchmark. However, the adverse-weather evaluation loop is partially circular because the phenomenon and the ground truth share the same generative model. The fog model has some independent support (fitted to real fog-chamber recordings), and rain and snow use physically based drop-size distributions, so this is partial circularity (score 6) rather than complete equivalence. The paper's own conclusion acknowledges the need for real-world datasets, which supports reading the headline claims as simulation-domain results rather than validated real-world behavior.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The key unprovided inputs are the weather simulation realism (from the authors' own prior work) and the noise-labeling assumption. The simulation parameters are hand-chosen ranges, not fitted to external data, but they directly determine the noise levels and hence the headline bandwidth savings. No new physical entities are introduced.

free parameters (6)
  • Snow precipitation rate = 5 to 20 mm/h (sampled per scenario)
    Chosen by hand to vary snow intensity; directly controls the amount of noise voxels and thus the measured bandwidth reduction.
  • Snowflake density = 500 to 2000 m^-3
    Chosen to cover low to high snow impact; affects noise density.
  • Snowflake scaling factor = 2 to 5
    Determines snowflake size distribution; influences noise characteristics.
  • Rain precipitation rate = 20 to 50 mm/h
    Chosen to range from moderate to heavy rain.
  • Raindrop density = 1000 to 2000 m^-3
    Sets the number of simulated raindrops per volume.
  • Fog viewing distance = Light fog: 70-200 m; Dense fog: 30-100 m
    The maximum viewing distance parameter of the probabilistic fog model; shorter distances inject more noise.
assumptions (4)
  • domain assumption The weather simulation models from [5] produce realistic LiDAR noise for rain, snow, and fog.
    The entire evaluation rests on this. The paper references [5] for the models and gives brief descriptions in Sec. IV, but does not validate them against real adverse-weather LiDAR data in this work.
  • domain assumption Voxel-level noise labels computed by majority vote over point labels are correct.
    Sec. VI-A states 'During voxelization, these labels are propagated to voxels by majority vote to provide the voxel-level ground truth.' This assumes voxels are homogeneous in noise status, which may fail at object boundaries or mixed voxels.
  • domain assumption The OPV2V dataset is a representative benchmark for collective perception.
    OPV2V is used as the base dataset; it is itself a simulated dataset (CARLA), so the results inherit its synthetic sensor characteristics.
  • domain assumption The scatter operation (element-wise maximum) preserves information needed for downstream detection.
    Sec. V-A1 describes the scattering as a design choice; no theoretical or empirical support is provided for its optimality.

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Cite this review

Pith. "Pith review of DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising." pith.science (2026). https://pith.science/paper/2CXXYRHK

@misc{pith2026250706976,
  author       = {Pith},
  title        = {Pith review of: DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2CXXYRHK}},
  note         = {Machine review of arXiv:2507.06976}
}
read the original abstract

While automated vehicles hold the potential to significantly reduce traffic accidents, their perception systems remain vulnerable to sensor degradation caused by adverse weather and environmental occlusions. Collective perception, which enables vehicles to share information, offers a promising approach to overcoming these limitations. However, to this date collective perception in adverse weather is mostly unstudied. Therefore, we conduct the first study of LiDAR-based collective perception under diverse weather conditions and present a novel multi-task architecture for LiDAR-based collective perception under adverse weather. Adverse weather conditions can not only degrade perception capabilities, but also negatively affect bandwidth requirements and latency due to the introduced noise that is also transmitted and processed. Denoising prior to communication can effectively mitigate these issues. Therefore, we propose DenoiseCP-Net, a novel multi-task architecture for LiDAR-based collective perception under adverse weather conditions. DenoiseCP-Net integrates voxel-level noise filtering and object detection into a unified sparse convolution backbone, eliminating redundant computations associated with two-stage pipelines. This design not only reduces inference latency and computational cost but also minimizes communication overhead by removing non-informative noise. We extended the well-known OPV2V dataset by simulating rain, snow, and fog using our realistic weather simulation models. We demonstrate that DenoiseCP-Net achieves near-perfect denoising accuracy in adverse weather, reduces the bandwidth requirements by up to 23.6% while maintaining the same detection accuracy and reducing the inference latency for cooperative vehicles.

Figures

Figures reproduced from arXiv: 2507.06976 by the authors.

Figure 1
Figure 1. LiDAR point clouds from the OPV2V [7] in bird’s-eye view. (a) Original point cloud, (b) the point cloud with [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Architecture of DenoiseCP-Net. The shared backbone takes the noisy ego voxel grid as input and extracts high [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Works this paper leans on

32 extracted references · 29 canonical work pages

  1. [1]

    Road traffic injuries,

    World Health Organization, “Road traffic injuries,” 2023, [On- line]. Available: https://www.who.int/news-room/fact-sheets/detail/road- traffic-injuries, [Accessed: March 21, 2025]

    work page 2023

  2. [2]

    Enhancing robustness of LiDAR-Based perception in adverse weather using point cloud augmentations,

    S. Teufel, J. Gamerdinger, G. V olk, C. Gerum, and O. Bringmann, “Enhancing robustness of LiDAR-Based perception in adverse weather using point cloud augmentations,” in 2023 IEEE Intelligent V ehicles Symposium (IV) (IEEE IV 2023) , Anchorage, USA, June 2023

    work page 2023

  3. [3]

    Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations,

    G. V olk, S. M¨uller, A. v. Bernuth, D. Hospach, and O. Bringmann, “Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations,” in2019 IEEE Intelligent Transportation Systems Conference (ITSC) , Oct 2019, pp. 285–292

    work page 2019

  4. [4]

    Mr3d-net: Dynamic multi-resolution 3d sparse voxel grid fusion for lidar-based collective perception,

    S. Teufel, J. Gamerdinger, G. V olk, and O. Bringmann, “Mr3d-net: Dynamic multi-resolution 3d sparse voxel grid fusion for lidar-based collective perception,” in 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) , 2024, pp. 1629–1634

    work page 2024

  5. [5]

    Simulating realistic rain, snow, and fog variations for comprehensive performance characterization of lidar perception,

    S. Teufel, G. V olk, A. V on Bernuth, and O. Bringmann, “Simulating realistic rain, snow, and fog variations for comprehensive performance characterization of lidar perception,” in 2022 IEEE 95th V ehicular Technology Conference:(VTC2022-Spring). IEEE, 2022, pp. 1–7

    work page 2022

  6. [6]

    Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds,

    Q. Chen, S. Tang, Q. Yang, and S. Fu, “Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds,” in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2019, pp. 514–524

    work page 2019

  7. [7]

    OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to- vehicle communication,

    R. Xu, H. Xiang, X. Xia, X. Han, J. Li, and J. Ma, “OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to- vehicle communication,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2583–2589

    work page 2022

  8. [8]

    F-Cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds,

    Q. Chen, X. Ma, S. Tang, J. Guo, Q. Yang, and S. Fu, “F-Cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds,” in Proceedings of the 4th ACM/IEEE Symposium on Edge Computing , 2019, pp. 88–100

    work page 2019

Show all 32 references
  1. [9]

    Pillargrid: Deep learning-based cooperative perception for 3d object detection from onboard-roadside lidar,

    Z. Bai, G. Wu, M. J. Barth, Y . Liu, E. A. Sisbot, and K. Oguchi, “Pillargrid: Deep learning-based cooperative perception for 3d object detection from onboard-roadside lidar,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) , 2022, pp. 1743–1749

    2022

  2. [10]

    Pointpillars: Fast encoders for object detection from point clouds,

    A. H. Lang, S. V ora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 12 697–12 705

    2019

  3. [11]

    Keypoints-based deep feature fusion for cooperative vehicle detection of autonomous driving,

    Y . Yuan, H. Cheng, and M. Sester, “Keypoints-based deep feature fusion for cooperative vehicle detection of autonomous driving,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 3054–3061, 2022

    2022

  4. [12]

    Bridging the domain gap for multi-agent perception,

    R. Xu, J. Li, X. Dong, H. Yu, and J. Ma, “Bridging the domain gap for multi-agent perception,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 6035–6042

    2023

  5. [13]

    Weighted boxes fusion: Ensembling boxes from different object detection models,

    R. Solovyev, W. Wang, and T. Gabruseva, “Weighted boxes fusion: Ensembling boxes from different object detection models,” Image and Vision Computing, vol. 107, p. 104117, 2021

    2021

  6. [14]

    A track-to- track association method for automotive perception systems,

    A. Houenou, P. Bonnifait, V . Cherfaoui, and J.-F. Boissou, “A track-to- track association method for automotive perception systems,” in 2012 IEEE Intelligent V ehicles Symposium . Alcal de Henares , Madrid, Spain: IEEE, June 2012, pp. 704–710

    2012

  7. [15]

    A generic video and radar data fusion system for improved target selection,

    D. M ¨uller, J. Pauli, M. Meuter, L. Ghosh, and S. M ¨uller-Schneiders, “A generic video and radar data fusion system for improved target selection,” in IEEE Intelligent V ehicles Symposium (IV) . IEEE, 2011

    2011

  8. [16]

    Aeberhard, Object-level Fusion for Surround Environment Percep- tion in Automated Driving Applications , ser

    M. Aeberhard, Object-level Fusion for Surround Environment Percep- tion in Automated Driving Applications , ser. Fortschrittberichte VDI / 12: Verkehrstechnik, Fahrzeugtechnik. VDI Verlag GmbH, 2017

    2017

  9. [17]

    Heterogeneous track-to-track fusion using equivalent measurement and unscented transform,

    C. Allig and G. Wanielik, “Heterogeneous track-to-track fusion using equivalent measurement and unscented transform,” in 2018 21st International Conference on Information Fusion (FUSION) , 2018

    2018

  10. [18]

    Environment-aware Development of Robust Vision-based Cooperative Perception Systems,

    G. V olk, A. von Bemuth, and O. Bringmann, “Environment-aware Development of Robust Vision-based Cooperative Perception Systems,” in 2019 IEEE Intelligent V ehicles Symposium (IV) . Paris, France: IEEE, June 2019, pp. 126–133

    2019

  11. [19]

    Infrastructure- supported perception and track-level fusion using edge computing,

    M. Gabb, H. Digel, T. M ¨uller, and R.-W. Henn, “Infrastructure- supported perception and track-level fusion using edge computing,” in 2019 IEEE Intelligent V ehicles Symposium (IV) . IEEE, 2019

    2019

  12. [20]

    Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving,

    G. V olk, Q. Delooz, F. A. Schiegg, A. V on Bernuth, A. Festag, and O. Bringmann, “Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) , 2021

    2021

  13. [21]

    Collective pv-rcnn: A novel fusion technique using collective detections for enhanced local lidar-based perception,

    S. Teufel, J. Gamerdinger, G. V olk, and O. Bringmann, “Collective pv-rcnn: A novel fusion technique using collective detections for enhanced local lidar-based perception,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2023, pp...

    2023

  14. [22]

    Collective perception datasets for autonomous driving: A comprehen- sive review,

    S. Teufel, J. Gamerdinger, J.-P. Kirchner, G. V olk, and O. Bringmann, “Collective perception datasets for autonomous driving: A comprehen- sive review,” in 2024 IEEE Intelligent V ehicles Symposium (IV) . IEEE, 2024, pp. 1548–1555

    2024

  15. [23]

    Weather-aware collaborative perception with uncertainty reduction,

    P. Jiang, X. Deng, W. Wu, L. Lin, X. Chen, C. Chen, and S. Wan, “Weather-aware collaborative perception with uncertainty reduction,” IEEE Transactions on Intelligent Transportation Systems , 2024

    2024

  16. [24]

    V2x-r: Cooperative lidar-4d radar fusion for 3d object detection with denoising diffusion,

    X. Huang, J. Wang, Q. Xia, S. Chen, B. Yang, X. Li, C. Wang, and C. Wen, “V2x-r: Cooperative lidar-4d radar fusion for 3d object detection with denoising diffusion,” arXiv preprint arXiv:2411.08402 , 2024

    2024 arXiv

  17. [25]

    The lognormal fit to raindrop spectra from frontal convective clouds in israel,

    G. Feingold and Z. Levin, “The lognormal fit to raindrop spectra from frontal convective clouds in israel,” Journal of climate and applied meteorology, pp. 1346–1363, 1986

    1986

  18. [26]

    The distribution with size of aggregate snowflakes,

    K. Gunn and J. Marshall, “The distribution with size of aggregate snowflakes,” Journal of Atmospheric Sciences , vol. 15, no. 5, pp. 452– 461, 1958

    1958

  19. [27]

    Snow size spectra and radar reflectivity,

    R. Sekhon and R. Srivastava, “Snow size spectra and radar reflectivity,” Journal of Atmospheric Sciences , vol. 27, no. 2, pp. 299–307, 1970

    1970

  20. [28]

    Deep learning for lidar point clouds in autonomous driving: A review,

    Y . Li, L. Ma, Z. Zhong, F. Liu, M. A. Chapman, D. Cao, and J. Li, “Deep learning for lidar point clouds in autonomous driving: A review,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 8, pp. 3412–3432, 2020

    2020

  21. [29]

    S2s-net: Addressing the domain gap of heterogeneous sensor systems in lidar-based collective perception,

    S. Teufel, J. Gamerdinger, and O. Bringmann, “S2s-net: Addressing the domain gap of heterogeneous sensor systems in lidar-based collective perception,” arXiv preprint arXiv:2504.17399 , 2025

    2025

  22. [30]

    PV-RCNN++: Point-voxel feature set abstraction with local vector representation for 3d object detection,

    S. Shi, L. Jiang, J. Deng, Z. Wang, C. Guo, J. Shi, X. Wang, and H. Li, “PV-RCNN++: Point-voxel feature set abstraction with local vector representation for 3d object detection,” International Journal of Computer Vision, vol. 131, no. 2, pp. 531–551, 2023

    2023

  23. [31]

    Cnn-based lidar point cloud de-noising in adverse weather,

    R. Heinzler, F. Piewak, P. Schindler, and W. Stork, “Cnn-based lidar point cloud de-noising in adverse weather,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2514–2521, 2020

    2020

  24. [32]

    Triplemixer: A 3d point cloud denoising model for adverse weather,

    X. Zhao, C. Wen, Y . Wang, H. Bai, and W. Dou, “Triplemixer: A 3d point cloud denoising model for adverse weather,” arXiv preprint arXiv:2408.13802, 2024

    2024 arXiv

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Reviewed August 6, 2026 · model on record in the stance chip above.