Pith. sign in

REVIEW 4 major objections 7 minor 54 references

WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion

T0 review · 4 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read WeatherGen generates fog, rain, and snow LiDAR point clouds in one diffusion framework, and the synthetic scans improve 3D detection when they replace part of real training data.

desk verdict A genuinely new unified learned generator for adverse-weather LiDAR with a sensible spider-mamba scan, but the missing appendix and underspecified MDP equation make the reported gains hard to verify. read the letter →

arxiv 2504.13561 v1 pith:TVKPGEDY submitted 2025-04-18 cs.CV

Yang Wu , Yun Zhu , Kaihua Zhang , Jianjun Qian , Jin Xie , Jian Yang This is my paper · ORCID
classification cs.CV
keywords LiDARpointcloudgenerationadverseweathersimulationdiffusionmodelsstatespacerangemap3Dobjectdetectioncontrollable
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

WeatherGen sets out to establish that diverse-weather LiDAR data does not require separate physics simulators or costly real collection: one diffusion model, pre-trained on synthetic maps and fine-tuned on a small real set, can generate fog, rain, snow, and clear scans at usable fidelity. The paper's contribution is a full pipeline consisting of a learnable map-based data producer, a spider mamba generator that scans along LiDAR beam circles, a latent feature aligner, and a contrastive weather controller, with ablations claiming that each piece earns its keep. A reader should care because adverse-weather LiDAR is scarce and expensive; if the claim holds, replacing only 7.4% of a clear-weather training set with generated adverse-weather frames can measurably improve a 3D detector under fog, snow, and rain. The paper supports the claim with distributional metrics in three representations and with detection experiments on a real fog, rain, and snow LiDAR benchmark.

What carries the argument

The load-bearing object is the range map together with the spider mamba scan. LiDAR scans are projected to a range map whose rows are beam circles and whose columns are central rays; the spider mamba generator runs a state-space scan along those rows and columns at point level rather than over image patches, so long-range sensor structure is preserved during denoising. The other essential mechanism is the map-based data producer of Equation (3), which converts a clear range map into a weather map using a hand-set distance threshold $r_w$, a Bernoulli drop function, additive noise, and a learned mask, and is then frozen during fine-tuning. The latent feature aligner and the contrastive weather controller supply real-data alignment and weather-discriminative conditioning, and the total training loss is the sum of their three losses.

What would settle it

Run the same pre-train-then-fine-tune recipe with $r_w$ varied across a wide range, for example one-tenth and ten times the values used in the paper, while changing nothing else. If the distributional metrics and the dense-fog and heavy-snow detection AP stay roughly constant, the map-based producer's specific threshold is not the source of WeatherGen's gain; if the metrics swing sharply, the producer is load-bearing.

Watch

Extended reading notes

Core claim

WeatherGen's central claim is that LiDAR point-cloud generation under multiple adverse weathers should be treated as one learnable distribution rather than as separate physical models. Starting from a clear-weather range map, the map-based data producer turns that map into fog, rain, and snow range maps using a hand-set distance threshold, a learned mask, Bernoulli point dropout, and random noise, thereby supplying enough pre-training data. A diffusion backbone then denoises these maps: the spider mamba generator scans range-map rows and columns, which correspond to LiDAR beam circles and central rays, so the annular structure of the sensor is preserved; the latent feature aligner pushes the one-step denoised output toward real adverse-weather latents; and the contrastive weather controller ties the weather code to text embeddings of weather names so the four modes stay distinct. After fine-tuning on a small real dataset, the model produces scans whose distributional distances to real data are lower than those of single-weather simulators, and a 256-frame-per-weather mini-dataset improves a 3D detector under dense fog, heavy snow, and rain when it supplies 7.4% of the training data.

Load-bearing premise

The load-bearing premise is that the hand-set distance threshold $r_w$ in Equation (3), along with Bernoulli dropout and the learned mask, captures fog, rain, and snow well enough that pre-training on those maps helps rather than hurts; the paper defers the actual $r_w$ values to an appendix that is not present in the submitted text.

Editorial extensions

If this is right

  • A single WeatherGen model can output clear, fog, rain, and snow scans from one set of weights, removing the need for per-weather physics simulators.
  • The pre-training-plus-fine-tuning recipe means a few hundred real adverse-weather frames can be enough to specialize a generator, because the map-based producer supplies the bulk of the training signal.
  • Replacing only 7.4% of a clear-weather training set with generated adverse-weather frames can raise detector performance on dense fog, heavy snow, and rain, making generated data a scalable augmentation channel.
  • The mini-weather dataset can be constructed in days rather than the years cited for real collection, making adverse-weather evaluation accessible without new field campaigns.
  • Because WeatherGen also runs as an unconditional clear-weather generator, the same architecture serves both standard LiDAR generation and weather-conditioned generation.
  • pith_inferences: These are the editor's own extensions, not the paper's claims.

Reading between the lines

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

  • A direct comparison against a non-learnable version of the map-based producer, with fixed masks and no learned component, would show how much of the reported gain comes from learning rather than from the hand-set threshold and dropout; the reported ablations only remove the producer entirely.
  • The spider mamba's row-and-column scanning prior is not specific to generation and could be transplanted to range-map completion, densification, or semantic segmentation, where preserving beam circles also matters.
  • Because the weather control is anchored to language embeddings, adding new weather descriptions such as light drizzle or blizzard may require only new text prompts rather than new training data.
  • The mini-weather gain is measured with one detector on one fog, rain, and snow benchmark; checking whether the substitution effect transfers to other detectors and to mixed-weather training sets would settle how general the improvement is.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 7 minor

Summary. The paper proposes WeatherGen, a diffusion-based framework for generating LiDAR point clouds under multiple adverse weather conditions. It combines a map-based data producer (MDP) for pretraining, a Spider Mamba generator (SMG) that scans range-map rows and columns, a latent feature aligner (LFA) that transfers real-world knowledge, and a contrastive learning-based controller (CLC) for weather control. Experiments on KITTI-360 and Seeing Through Fog report improved fidelity metrics over prior simulators and generative models, and a constructed mini-weather dataset is shown to improve PointPillars detection under fog, snow, and rain.

Significance. If the fidelity claims hold, WeatherGen would be a practical path to scaling adverse-weather LiDAR data without costly collection, and the beam/ray scanning design of the SMG is well motivated by the LiDAR imaging process. The paper ships code, includes ablations for each component, and evaluates downstream detection utility. However, the central fidelity comparison rests on under-specified pretraining details, small-sample point estimates, and unreplicated detector results, so the significance is conditional on those issues being resolved.

major comments (4)
  1. [Section 3.2, Eq. (3)] The MDP is a load-bearing component, as Table 3 shows that removing it raises FPD from 59.28 to 84.58, yet Eq. (3) is not fully specified: the masks Me1 and Me2 appear in the equation but are never separately defined, Rn is introduced only as 'random noise related to r_w' with no distribution or scale, and the adjustable parameter r_w is deferred to an appendix that is absent from the submitted version. Without these settings the pretraining premise cannot be audited, and the source of the reported fidelity gain cannot be attributed to the proposed learned components.
  2. [Section 3.2, Eq. (9)] The text states that Md is a mask 'learned through Equation 9,' but Eq. (9) defines the total loss L = LSMG + LLFA + LCLC, and none of these terms provides an explicit supervision signal for Md. Unless a separate objective for Md exists and is described, the claim that MDP 'adaptively align[s] with real-world distribution' is not supported by the stated loss.
  3. [Section 4.3, Table 2] Weather-conditional generation is evaluated with only 200 generated samples per condition, and Tables 1–3 report single point estimates for FPD, FRD, MMD, and JSD without error bars or repeated sampling. Since diffusion sampling is stochastic, the reported margins, such as WeatherGen(Snow) FPD 59.28 versus LSS(Snow) 106.37, cannot be judged as significant without variance information; the authors should provide standard deviations or confidence intervals over multiple generation runs.
  4. [Section 4.5, Tables 4–6] The downstream detection improvements are small and unreplicated: for example, Table 4 shows +1.39 and +2.08 AP over the 'None' baseline in the 0–80 m and 0–30 m dense-fog rows, and Table 5 shows only +0.25 AP for the 50–80 m snow row. No variance over detector training is reported, so it is unclear whether these gains are beyond training stochasticity; at minimum, multiple seeded runs or a significance test are needed to support the mini-weather claim.
minor comments (7)
  1. [Section 3.3, Eq. (4)] The projection in Eq. (4) is written as [F1_i W; ...; Fj_i W], but the superscript indexing of patches and the relationship among J, C, and the number of patches is not defined; please clarify the reshaping step.
  2. [Figure 2] The label 'Per tubed' in Figure 2 appears to be a typo, and the flow arrows for CLC and LFA are not described in the caption, making the figure hard to follow.
  3. [Table 3] In Table 3, the checkmark patterns for the first and last rows are visually inconsistent (e.g., rows with '✓ ✓ ✓- -' and '✓ ✓ ✓- ✓' are difficult to parse); please use explicit Yes/No columns or a clearer legend.
  4. [Section 4.1] The evaluation description says 10,000 generated samples are used for unconditional generation and 200 for weather-conditional generation, but it does not state whether the same real-world test set is used for both, nor how many real-world samples are used in the weather-conditional metrics; please specify the exact protocol.
  5. [Section 4.6, Figure 8] The percentages in Figure 8, such as '-7.38% FRD' and '+0.51%', are not defined in the text; please state the reference baseline for each percentage.
  6. [Appendix references] The paper refers to 'the Appendix' multiple times (for r_w settings, visual structures, densification details), but no appendix is included in the arXiv v1 submission; this is a presentation issue that should be fixed by including the supplementary material.
  7. [Section 3.4, Eq. (6)] The KL divergence in Eq. (6) is notationally ambiguous because the two distributions are defined on different latent variables (zreal and zgen); please specify how the alignment is computed across the two latent spaces.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's learned components are trained against real-world data and evaluated on held-out splits, not defined in terms of their own outputs.

full rationale

The derivation chain in WeatherGen is not circular. The map-based data producer (Eq. 3) is a parameterized simulation used to create pre-training data, and its learnable mask Md is trained through the total loss (Eq. 9); this is a fitted input to training, not a relabeled output of the evaluation. The latent feature aligner (Eq. 6) applies a KL divergence between latents of one-step generated samples and real-world diverse-weather samples during training, but all fidelity metrics (FPD, FRD, MMD, JSD) are computed against held-out real-world test splits using external feature extractors, so the reported quality is not forced by construction. The contrastive learning-based controller is trained with CLIP text supervision to shape control signals, and the ablation study isolates MDP, SMG, CLC, LFA, and fine-tuning contributions. The paper's self-citations (e.g., Text2LiDAR [46], [45]) appear in related work and as supporting intuition for latent-space expansion; they are not load-bearing premises on which the main result depends. The under-specification of Eq. 3 (unreported r_w values, undefined Me1/Me2, and the absent appendix) is a completeness and reproducibility concern, not evidence of circularity. Overall, the generation quality and downstream detection gains are empirical claims supported by held-out evaluations, so the paper merits a circularity score of 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The free parameters are a hand-set weather severity and one empirical loss weight; the axioms are standard domain assumptions for range-map-based LiDAR generation, one-step diffusion alignment, CLIP-guided control, and distributional fidelity metrics. The missing r_w values and missing appendix make the MDP assumption the least externally checkable.

free parameters (2)
  • r_w = not reported in main text, deferred to Appendix
    Hand-set weather severity threshold in the MDP formula (Eq. 3) that controls distance cutoff and Bernoulli drop rate for snow, fog, and rain. Values are not given in the paper.
  • beta in CLC = 0.2
    Empirical weighting parameter in the information-bottleneck objective in Equation 8, chosen by hand without sensitivity analysis.
assumptions (4)
  • domain assumption Projecting LiDAR point clouds onto a range map loses no data needed for generation.
    Invoked in Section 2 based on refs [22, 23]. The whole pipeline operates on range maps, so this losslessness is load-bearing.
  • domain assumption One-step generation from the predicted noise carries enough weather feature information for the latent feature aligner.
    Equation 6 aligns real and generated latents using one-step generation; the paper assumes this noisy output is sufficient, citing [20, 41] without direct validation.
  • domain assumption Frozen CLIP text embeddings for weather prompt words provide compact and discriminative semantic control signals.
    Section 3.4 relies on CLIP text embeddings for 'snow', 'rain', 'fog', and 'clear' without reporting the exact prompts or validating their semantic separation.
  • domain assumption Distributional distances computed by pretrained RangeNet and PointNet on generated versus real samples measure generation fidelity.
    Section 4.1 uses FPD, FRD, MMD, and JSD as the primary fidelity metrics, assuming these proxies reflect the realism that matters for downstream tasks.

how reviews work

0 comments
Cite this review

Pith. "Pith review of WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion." pith.science (2026). https://pith.science/paper/TVKPGEDY

@misc{pith2026250413561,
  author       = {Pith},
  title        = {Pith review of: WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TVKPGEDY}},
  note         = {Machine review of arXiv:2504.13561}
}
read the original abstract

3D scene perception demands a large amount of adverse-weather LiDAR data, yet the cost of LiDAR data collection presents a significant scaling-up challenge. To this end, a series of LiDAR simulators have been proposed. Yet, they can only simulate a single adverse weather with a single physical model, and the fidelity of the generated data is quite limited. This paper presents WeatherGen, the first unified diverse-weather LiDAR data diffusion generation framework, significantly improving fidelity. Specifically, we first design a map-based data producer, which can provide a vast amount of high-quality diverse-weather data for training purposes. Then, we utilize the diffusion-denoising paradigm to construct a diffusion model. Among them, we propose a spider mamba generator to restore the disturbed diverse weather data gradually. The spider mamba models the feature interactions by scanning the LiDAR beam circle or central ray, excellently maintaining the physical structure of the LiDAR data. Subsequently, following the generator to transfer real-world knowledge, we design a latent feature aligner. Afterward, we devise a contrastive learning-based controller, which equips weather control signals with compact semantic knowledge through language supervision, guiding the diffusion model to generate more discriminative data. Extensive evaluations demonstrate the high generation quality of WeatherGen. Through WeatherGen, we construct the mini-weather dataset, promoting the performance of the downstream task under adverse weather conditions. Code is available: https://github.com/wuyang98/weathergen

Figures

Figures reproduced from arXiv: 2504.13561 by the authors.

Figure 1
Figure 1. (a) Previous simulation-based methods [3, 9, 10, 15] can only provide a single non-learnable simulator for a single weather condition. Due to the complexity of optical propagation, previ￾ous simulated data all suffer from insufficient fidelity issues. (b) Our method is the first unified generative framework. Through learning, the generated data has higher fidelity and can be more conducive to promoting downstream ta… view at source ↗
Figure 2
Figure 2. The pipeline of WeatherGen. It has three core components. An MDP to produce high-quality training data that is closer to [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. (a) Spider mamba scans model features along the LiDAR [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Visual comparisons with competitive generation methods on KITTI-360 [ [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Visual comparisons of real-world diverse weather LiDAR data and generated results on Seeing Through Fog [ [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: The t-SNE plots of the real-world data [ [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: The composition of the constructed mini-weather [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Model efficiency comparisons. WeatherGen balances [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 31 canonical work pages

  1. [1]

    See- ing through fog without seeing fog: Deep multimodal sen- sor fusion in unseen adverse weather

    Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide. See- ing through fog without seeing fog: Deep multimodal sen- sor fusion in unseen adverse weather. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11682–11692, 2020. 1, 2, 5, 6, 7, 8

    work page 2020

  2. [2]

    Deep generative modeling of lidar data

    Lucas Caccia, Herke Van Hoof, Aaron Courville, and Joelle Pineau. Deep generative modeling of lidar data. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5034–5040. IEEE, 2019. 6

    work page 2019

  3. [3]

    De-noising of lidar point clouds corrupted by snowfall

    Nicholas Charron, Stephen Phillips, and Steven L Waslander. De-noising of lidar point clouds corrupted by snowfall. In 2018 15th Conference on Computer and Robot Vision (CRV), pages 254–261. IEEE, 2018. 1, 2, 4, 7, 8

    work page 2018

  4. [4]

    Understanding ro- bustness of visual state space models for image classifica- tion

    Chengbin Du, Yanxi Li, and Chang Xu. Understanding ro- bustness of visual state space models for image classifica- tion. arXiv preprint arXiv:2403.10935, 2024. 3

    arXiv 2024

  5. [5]

    Mamba: Linear-time sequence modeling with selective state spaces

    Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023. 2

    arXiv 2023

  6. [6]

    Efficiently modeling long sequences with structured state spaces

    Albert Gu, Karan Goel, and Christopher R ´e. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396, 2021. 2, 3

    arXiv 2021

  7. [7]

    On the parameterization and initialization of diagonal state space models

    Albert Gu, Karan Goel, Ankit Gupta, and Christopher R ´e. On the parameterization and initialization of diagonal state space models. Advances in Neural Information Processing Systems, 35:35971–35983, 2022. 2

    2022

  8. [8]

    Diagonal state spaces are as effective as structured state spaces

    Ankit Gupta, Albert Gu, and Jonathan Berant. Diagonal state spaces are as effective as structured state spaces. Advances in Neural Information Processing Systems, 35:22982–22994,

Show all 54 references
  1. [9]

    Fog simulation on real lidar point clouds for 3d object detection in adverse weather

    Martin Hahner, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Fog simulation on real lidar point clouds for 3d object detection in adverse weather. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 15283–15292, 2021. 1, 2, 4, 6, 7, 8

    2021

  2. [10]

    Lidar snowfall simulation for robust 3d object detection

    Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, and Luc Van Gool. Lidar snowfall simulation for robust 3d object detection. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16364–16374, 2022. 1...

    2022

  3. [11]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 3

    2020

  4. [12]

    Rangeldm: Fast realistic lidar point cloud generation

    Qianjiang Hu, Zhimin Zhang, and Wei Hu. Rangeldm: Fast realistic lidar point cloud generation. In European Confer- ence on Computer Vision , pages 115–135. Springer, 2024. 2

    2024

  5. [13]

    A survey on information bottleneck

    Shizhe Hu, Zhengzheng Lou, Xiaoqiang Yan, and Yangdong Ye. A survey on information bottleneck. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 5

    2024

  6. [14]

    Zigma: Zigzag mamba diffusion model

    Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, Johannes Fischer, and Bjorn Ommer. Zigma: Zigzag mamba diffusion model. arXiv preprint arXiv:2403.13802, 2024. 3, 4

    2024 arXiv

  7. [15]

    Lidar light scattering augmentation (lisa): Physics-based simulation of adverse weather conditions for 3d object detection

    Velat Kilic, Deepti Hegde, Vishwanath Sindagi, A Brinton Cooper, Mark A Foster, and Vishal M Patel. Lidar light scattering augmentation (lisa): Physics-based simulation of adverse weather conditions for 3d object detection. arXiv preprint arXiv:2107.07004, 2021. 1, 2, 4, 6, 7, 8

    2021 arXiv

  8. [16]

    Robo3d: Towards robust and reliable 3d perception against corruptions

    Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wen- wei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Robo3d: Towards robust and reliable 3d perception against corruptions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 19994–20006...

    2023

  9. [17]

    Pointpillars: Fast encoders for object detection from point clouds

    Alex H Lang, Sourabh V ora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12697–12705, 2019. 7

    2019

  10. [18]

    Point- mamba: A simple state space model for point cloud analysis

    Dingkang Liang, Xin Zhou, Xinyu Wang, Xingkui Zhu, Wei Xu, Zhikang Zou, Xiaoqing Ye, and Xiang Bai. Point- mamba: A simple state space model for point cloud analysis. arXiv preprint arXiv:2402.10739, 2024. 3

    2024 arXiv

  11. [19]

    Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d

    Yiyi Liao, Jun Xie, and Andreas Geiger. Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):3292–3310, 2022. 5, 6

    2022

  12. [20]

    Instaflow: One step is enough for high-quality diffusion- based text-to-image generation

    Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, et al. Instaflow: One step is enough for high-quality diffusion- based text-to-image generation. In The Twelfth International Conference on Learning Representations, 2023. 5

    2023

  13. [21]

    Vmamba: Visual state space model

    Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu. Vmamba: Visual state space model. arXiv preprint arXiv:2401.10166,

    arXiv

  14. [22]

    Lasernet: An efficient probabilistic 3d object detector for autonomous driving

    Gregory P Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi- Gonzalez, and Carl K Wellington. Lasernet: An efficient probabilistic 3d object detector for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 12677–12686, 2019. 2

    2019

  15. [23]

    Rangenet++: Fast and accurate lidar semantic segmentation

    Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyrill Stachniss. Rangenet++: Fast and accurate lidar semantic segmentation. In 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 4213–4220. IEEE, 2019. 2, 5

    2019

  16. [24]

    Learning to drop points for lidar scan synthesis

    Kazuto Nakashima and Ryo Kurazume. Learning to drop points for lidar scan synthesis. In 2021 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), pages 222–229. IEEE, 2021. 2

    2021

  17. [25]

    Lidar data synthesis with denoising diffusion probabilistic models.arXiv preprint arXiv:2309.09256, 2023

    Kazuto Nakashima and Ryo Kurazume. Lidar data synthesis with denoising diffusion probabilistic models.arXiv preprint arXiv:2309.09256, 2023. 2, 4, 5, 6, 8

    2023 arXiv

  18. [26]

    Generative range imaging for learning scene priors of 3d li- dar data

    Kazuto Nakashima, Yumi Iwashita, and Ryo Kurazume. Generative range imaging for learning scene priors of 3d li- dar data. In Proceedings of the IEEE/CVF Winter Confer- ence on Applications of Computer Vision, pages 1256–1266,

  19. [27]

    Resurrecting recurrent neural networks for long sequences

    Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fer- nando, Caglar Gulcehre, Razvan Pascanu, and Soham De. Resurrecting recurrent neural networks for long sequences. In International Conference on Machine Learning , pages 26670–26698. PMLR, 2023. 2

    2023

  20. [28]

    Ssm meets video diffusion models: Efficient video generation with structured state spaces

    Yuta Oshima, Shohei Taniguchi, Masahiro Suzuki, and Yu- taka Matsuo. Ssm meets video diffusion models: Efficient video generation with structured state spaces. arXiv preprint arXiv:2403.07711, 2024. 3

    2024 arXiv

  21. [29]

    Rethinking data augmentation for robust lidar seman- tic segmentation in adverse weather

    Junsung Park, Kyungmin Kim, and Hyunjung Shim. Rethinking data augmentation for robust lidar seman- tic segmentation in adverse weather. arXiv preprint arXiv:2407.02286, 2024. 1, 3, 4

    2024 arXiv

  22. [30]

    Pointnet: Deep learning on point sets for 3d classification and segmentation

    Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652–660,

  23. [31]

    A conditional denoising diffusion proba- bilistic model for point cloud upsampling

    Wentao Qu, Yuantian Shao, Lingwu Meng, Xiaoshui Huang, and Liang Xiao. A conditional denoising diffusion proba- bilistic model for point cloud upsampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20786–20795, 2024. 2

    2024

  24. [32]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, ...

    2021

  25. [33]

    Towards realistic scene generation with lidar diffusion models

    Haoxi Ran, Vitor Guizilini, and Yue Wang. Towards realistic scene generation with lidar diffusion models. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14738–14748, 2024. 2, 4, 6

    2024

  26. [34]

    A tutorial on bernoulli filters: theory, implementation and applications

    Branko Ristic, Ba-Tuong V o, Ba-Ngu V o, and Alfonso Fa- rina. A tutorial on bernoulli filters: theory, implementation and applications. IEEE Transactions on Signal Processing, 61(13):3406–3430, 2013. 4

    2013

  27. [35]

    la- belcloud: A lightweight domain-independent labeling tool for 3d object detection in point clouds

    Christoph Sager, Patrick Zschech, and Niklas K ¨uhl. la- belcloud: A lightweight domain-independent labeling tool for 3d object detection in point clouds. arXiv preprint arXiv:2103.04970, 2021. 7

    2021 arXiv

  28. [36]

    Model adaptation with synthetic and real data for semantic dense foggy scene understanding

    Christos Sakaridis, Dengxin Dai, Simon Hecker, and Luc Van Gool. Model adaptation with synthetic and real data for semantic dense foggy scene understanding. In Proceed- ings of the european conference on computer vision (ECCV), pages 687–704, 2018. 2

    2018

  29. [37]

    Seman- tic foggy scene understanding with synthetic data

    Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Seman- tic foggy scene understanding with synthetic data. Interna- tional Journal of Computer Vision, 126:973–992, 2018. 2

    2018

  30. [38]

    Projected gans converge faster

    Axel Sauer, Kashyap Chitta, Jens M ¨uller, and Andreas Geiger. Projected gans converge faster. Advances in Neural Information Processing Systems, 34:17480–17492, 2021. 6

    2021

  31. [39]

    4denoisenet: Adverse weather denoising from adjacent point clouds.IEEE Robotics and Automation Letters, 8(1):456–463, 2022

    Alvari Sepp ¨anen, Risto Ojala, and Kari Tammi. 4denoisenet: Adverse weather denoising from adjacent point clouds.IEEE Robotics and Automation Letters, 8(1):456–463, 2022. 3

    2022

  32. [40]

    Simplified state space layers for sequence modeling

    Jimmy TH Smith, Andrew Warrington, and Scott W Linder- man. Simplified state space layers for sequence modeling. arXiv preprint arXiv:2208.04933, 2022. 2

    2022 arXiv

  33. [41]

    Consistency models

    Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. In Proceedings of the 40th International Conference on Machine Learning , pages 32211–32252, 2023. 5

    2023

  34. [42]

    Rain rendering for evaluating and improving robustness to bad weather

    Maxime Tremblay, Shirsendu Sukanta Halder, Raoul De Charette, and Jean-Franc ¸ois Lalonde. Rain rendering for evaluating and improving robustness to bad weather. Inter- national Journal of Computer Vision, 129:341–360, 2021. 2

    2021

  35. [43]

    Disen- tangled representation learning

    Xin Wang, Hong Chen, Zihao Wu, Wenwu Zhu, et al. Disen- tangled representation learning. IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 2024. 2

    2024

  36. [44]

    Ultralight vm-unet: Parallel vision mamba significantly reduces parameters for skin lesion segmentation

    Renkai Wu, Yinghao Liu, Pengchen Liang, and Qing Chang. Ultralight vm-unet: Parallel vision mamba significantly reduces parameters for skin lesion segmentation. arXiv preprint arXiv:2403.20035, 2024. 4

    2024 arXiv

  37. [45]

    Co-salient object detection with uncertainty-aware group exchange-masking

    Yang Wu, Huihui Song, Bo Liu, Kaihua Zhang, and Dong Liu. Co-salient object detection with uncertainty-aware group exchange-masking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 19639–19648, 2023. 5

    2023

  38. [46]

    Text2lidar: Text-guided lidar point cloud generation via equirectangular transformer

    Yang Wu, Kaihua Zhang, Jianjun Qian, Jin Xie, and Jian Yang. Text2lidar: Text-guided lidar point cloud generation via equirectangular transformer. In European Conference on Computer Vision, pages 291–310. Springer, 2024. 2, 4, 5, 6, 8

    2024

  39. [47]

    Learning compact representations for lidar comple- tion and generation

    Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang, and Raquel Urtasun. Learning compact representations for lidar comple- tion and generation. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 1074–1083, 2023. 2

    2023

  40. [48]

    A survey on vision mamba: Models, applications and chal- lenges

    Rui Xu, Shu Yang, Yihui Wang, Bo Du, and Hao Chen. A survey on vision mamba: Models, applications and chal- lenges. arXiv preprint arXiv:2404.18861, 2024. 2

    2024 arXiv

  41. [49]

    Tri- perspective view decomposition for geometry-aware depth completion

    Zhiqiang Yan, Yuankai Lin, Kun Wang, Yupeng Zheng, Yufei Wang, Zhenyu Zhang, Jun Li, and Jian Yang. Tri- perspective view decomposition for geometry-aware depth completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4874– 4884, 2024. 2

    2024

  42. [50]

    P-mamba: Marrying perona malik diffusion with mamba for efficient pediatric echocar- diographic left ventricular segmentation

    Zi Ye and Tianxiang Chen. P-mamba: Marrying perona malik diffusion with mamba for efficient pediatric echocar- diographic left ventricular segmentation. arXiv preprint arXiv:2402.08506, 2024. 3

    2024 arXiv

  43. [51]

    Un- certainty inspired rgb-d saliency detection

    Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Saleh, Sadegh Aliakbarian, and Nick Barnes. Un- certainty inspired rgb-d saliency detection. IEEE transac- tions on pattern analysis and machine intelligence , 44(9): 5761–5779, 2021. 5

    2021

  44. [52]

    Vision mamba: Efficient visual representation learning with bidirectional state space model

    Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417, 2024. 2, 3, 4

    2024 arXiv

  45. [53]

    Spgroup3d: Su- perpoint grouping network for indoor 3d object detection

    Yun Zhu, Le Hui, Yaqi Shen, and Jin Xie. Spgroup3d: Su- perpoint grouping network for indoor 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelli- gence, pages 7811–7819, 2024. 2

    2024

  46. [54]

    Learning to generate realistic lidar point clouds

    Vlas Zyrianov, Xiyue Zhu, and Shenlong Wang. Learning to generate realistic lidar point clouds. InEuropean Conference on Computer Vision, pages 17–35. Springer, 2022. 2, 4, 5, 6, 8

    2022

Pith tools

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