Pith. sign in

REVIEW 4 major objections 6 minor 50 references

Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read Adverse weather systematically degrades LiDAR 3D trackers, and the proposed DRCT training framework recovers a substantial share of the lost performance.

desk verdict First adverse-weather benchmark for 3D LiDAR single-object tracking, with a clean evaluation of five trackers; the DRCT method is a modest synthetic-only improvement whose real-world transfer is unsupported. read the letter →

arxiv 2501.07133 v1 pith:GKD5EBFG submitted 2025-01-13 cs.CV

Xiantong Zhao , Xiuping Liu , Shengjing Tian , Yinan Han This is my paper · ORCID
classification cs.CV
keywords 3DsingleobjecttrackingLiDARpointcloudsadverseweatherrobustnessbenchmarkdomainrandomizationcontrastivelearningKITTI-ACADC-SOT
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

3D single object tracking in LiDAR point clouds is normally evaluated only on clear weather. This paper argues that this hides a serious robustness problem: rain, fog, and snow alter the point cloud distribution, and five representative trackers lose a large share of accuracy on a new adverse-weather benchmark. To make the argument, the authors build two synthetic benchmark sets, KITTI-A and nuScenes-A, with five intensity levels of rain, fog, and snow, and one real snowy set, CADC-SOT, filtered from an existing capture. They then identify three factors behind the failures: target distance, template shape corruption, and target shape corruption. Finally, they propose a dual-branch tracking framework, DRCT, that trains an auxiliary branch on domain-randomized point clouds and transfers robustness to the main branch through local geometric contrastive learning, improving both clean and weather-affected performance over a strong MBPTrack baseline.

What carries the argument

The argument rests on two constructed objects. The benchmark is three tracking datasets: KITTI-A and nuScenes-A, made by applying fog simulation and rain/snow simulation to clean KITTI and nuScenes validation sequences at five intensities, plus CADC-SOT, made by filtering the real snowy CADC scans to sequences whose targets have at least 10 points and more than four frames. The second object is DRCT, a dual-branch training framework built on MBPTrack: the primary branch processes the original clean point cloud, the auxiliary branch processes the same cloud after domain randomization (random noise addition, point dropout, and jittering), and a local geometric contrastive module aligns neighborhood features around common key points using ball query and max pooling, transferring corruption-robust features into the primary branch. A degradation-rate metric, defined as the ratio of weather-affected success or precision to the clean value, quantifies retained accuracy per weather type and level.

What would settle it

Run both MBPTrack and DRCT on a real-world rainy, foggy, and snowy tracking set collected with the same sensor geometry and compare their degradation rates; if DRCT's KITTI-A advantage does not persist, or if the synthetic degradation rates fail to reproduce on real weather, the benchmark's representativeness claim collapses.

Watch

Extended reading notes

Core claim

The paper's central claim is that state-of-the-art 3D single object trackers are not robust to adverse weather, and that the failure is systematic enough to be benchmarked and partly fixed. On KITTI-A, car tracking success drops by roughly a third across rain and snow and by 17-24% in fog, while fog cuts pedestrian success by 51-70%; on the sparser nuScenes-A, degradation is even larger and some pedestrian cases approach failure. The authors trace the drop to three causes: distance-dependent signal loss, corruption of the template point cloud, and corruption of the search-area target. They then present DRCT, a dual-branch extension of MBPTrack in which one branch sees clean data, the other sees point clouds corrupted by random noise, dropout, and jitter, and local geometric contrastive learning pulls the clean branch toward the robust representations of the randomized branch. DRCT improves MBPTrack's mean success by +2.51/+1.23/+1.88 points on KITTI-A cars under rain, fog, and snow, while also gaining +1.60/+1.11 on clean KITTI.

Load-bearing premise

The load-bearing premise is that the simulated rain, fog, and snow point clouds in KITTI-A and nuScenes-A are faithful enough to real adverse weather for the measured degradation rates and the DRCT improvement to transfer to physical conditions.

Editorial extensions

If this is right

  • Robustness evaluation under rain, fog, and snow should become a standard axis for 3D single object tracking, since clean-dataset rankings alone overstate real-world capability.
  • MBPTrack, the strongest of the five evaluated trackers on KITTI-A, still loses about a third of its car success in rain and snow, so even the best current methods are not weather-proof.
  • Sparser LiDAR (nuScenes 32-beam) suffers larger degradation than KITTI's 64-beam, especially for pedestrians, making weather robustness also a sensor-resolution problem.
  • DRCT's asymmetric dual-branch training with local geometric contrastive learning raises both clean and weather-affected performance over its MBPTrack baseline, while training directly on random domains alone does not.
  • Template shape corruption is a major failure driver: even small template changes produce large IOU drops, so robust template representation is key for future trackers.

Reading between the lines

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

  • The paper evaluates DRCT only on synthetic KITTI-A and never on the real-world CADC-SOT, so the most direct open test is whether the synthetic-trained gains transfer to real snow, where the paper itself finds no monotonic level-performance relationship.
  • The distance analysis suggests that denser LiDAR mitigates weather damage, implying beam-count augmentation or sensor-fusion input could be an explicit robustness lever beyond training strategy.
  • Because the synthetic weather is generated frame-by-frame from clean scans, dynamic physical effects such as snow accumulation on objects or wet-road returns are absent, so a real-weather benchmark including those effects may show different failure modes.
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 / 6 minor

Summary. This paper introduces a benchmark for evaluating 3D single-object tracking (3DSOT) under adverse weather, consisting of two synthetic datasets (KITTI-A and nuScenes-A, generated by applying fog, rain, and snow simulations to clean KITTI and nuScenes data) and one real-world dataset (CADC-SOT, filtered from CADC snowfall sequences). Five representative trackers (BAT, MMTrack, STNet, CXTrack, MBPTrack) are evaluated on the benchmark, and the reported degradation rates are used to analyze the impact of target distance, template shape corruption, and target shape corruption. The paper then proposes DRCT, a dual-branch extension of MBPTrack that combines domain randomization in an auxiliary branch with a local geometric contrastive learning module, and reports improved success/precision over the MBPTrack baseline on KITTI-A.

Significance. The paper addresses a genuine gap: no adverse-weather benchmark currently exists for 3D single-object tracking. The synthetic corruption pipeline builds on established physically motivated simulators, and the real-world CADC-SOT filtering is a useful resource that could support future robustness studies. The systematic evaluation of five trackers across weather types and intensity levels provides a useful reference point for the community. The DRCT experiment is also framed honestly as an improvement over an external baseline, MBPTrack, rather than over a quantity fitted from the data. However, the significance of the central robustness claim is currently limited by the absence of DRCT results on real-world data and by several specification gaps that prevent reproduction.

major comments (4)
  1. [Section III.D, Eq. (2) and Tables III, IV] Equation (2) defines DR as S_i,j / S_c, i.e., the retained performance ratio, but the values reported as DR in Tables III and IV are clearly computed as 1 - S_i,j / S_c. For example, MBPTrack car/rain in Table III has clean success 73.70 and average KITTI-A success 48.71; the reported DR is 0.34, which equals 1 - 48.71/73.70, whereas Eq. (2) would give 0.66. This internal inconsistency affects every degradation-rate claim in the paper, including the statement in Section IV.A.1 that car performance "decreases by 32%-42%." The definition or the table values must be corrected so that the reported numbers match the stated formula.
  2. [Section V.D and Section IV.B.1] DRCT is evaluated only on the synthetic KITTI-A split; Table VI contains no results on the real CADC-SOT dataset or on nuScenes-A. The abstract and conclusion claim that DRCT achieves "excellent performance in benchmarks," but the only evidence is a synthetic benchmark whose real-world validity is not established. This is especially problematic because Section IV.B.1 reports that on the real CADC-SOT data, "snow levels did not show a negative correlation with tracker performance under real weather conditions and snow-covered roads did not lead to performance degradation." That observation undercuts the premise that real adverse weather uniformly degrades tracking and leaves open the question of whether the synthetic KITTI-A improvements transfer to physically collected snowfall. The authors should either report DRCT on CADC-SOT or substantially qualify the claim that DRCT improves real-world adverse-weather tracking.
  3. [Section III.B, Algorithm 1, and Section V.E] Several parameters that are load-bearing for reproducing the benchmark and the method are not disclosed. Section III.B says the fog simulation sets the meteorological optical range to "five discrete values" and that rain and snow simulations set rainfall and snowfall rates, but no numerical values are given. Algorithm 1 depends on Nmax and the jitter bound a, and Eq. (4) depends on the neighbor count K, yet none of these values appears in the experimental section. The paper should report these values (or release the exact configuration code) so that the benchmark levels and the DRCT results are reproducible.
  4. [Section IV.A.1 and Table III] The narrative describes the pedestrian rain results as showing a "small performance drop except CXTrack," but Table III lists negative DR values for BAT (-0.05/-0.07) and near-zero values for MBPTrack (0.005/0.004) in the pedestrian rain rows. Negative degradation rates mean the trackers improved under rain, which is qualitatively different from a small drop and should be acknowledged and interpreted. This also raises a question about whether the degradation-rate formulation, even once corrected, is appropriate for cases where S_i,j exceeds S_c.
minor comments (6)
  1. [References] Reference [10] cites a paper on graph convolutional networks that is not the point-cloud DGCNN architecture used by MBPTrack and CXTrack; the appropriate citation is Wang et al., "Dynamic Graph CNN for Learning on Point Clouds," ACM Transactions on Graphics, 2019.
  2. [Section IV.A] There is a typo in the opening sentence: "nuScnenes-A" should be "nuScenes-A."
  3. [Table II caption] The caption contains a duplicated phrase: "THE NUMBER OF SCENARIOS AND FRAMES IN THE TABLE SHOWS THE NUMBER OF SCENARIOS AND FRAMES." It should be shortened to a single clause.
  4. [Figure 3] The vertical axis of Figure 3 is labeled "IOU deviation," but this quantity is not defined in Section III or Section IV. Please define it explicitly, including how it is aggregated over sequences and frames.
  5. [Table VII] The column labeled "RD" is not defined in the text, and the meaning of "KITTI" versus "Clean" in the first two columns is unclear. Clarify which split the "Clean" column refers to and what "RD" denotes.
  6. [Section V.C] The heading "Local Geometric Contrast Learning" appears to be missing the suffix "ive"; it should read "Local Geometric Contrastive Learning."

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark is independently constructed and DRCT is evaluated against an external baseline.

full rationale

The paper's two main contributions are (1) an adverse-weather 3D SOT benchmark and (2) a domain-randomization-plus-contrastive-learning method (DRCT). The synthetic benchmark splits are generated by applying independent weather simulation algorithms ([40], [41]) to KITTI and nuScenes, while the real-world CADC-SOT split is obtained by explicit filtering thresholds on the CADC dataset. The degradation-rate metric (Eq. 2) is a measured success/precision ratio against a clean baseline, not a quantity derived from the corruption parameters. DRCT's improvements in Table VI are reported relative to MBPTrack, an external baseline, on the synthetic KITTI-A benchmark; no parameter of DRCT is fitted to the evaluation set, and the domain-randomization perturbations are generic (noise, dropout, jitter) rather than calibrated to the test-time simulations. Self-citations (e.g., OST, SETD, class-agnostic tracking) appear only in related-work or method-context references and are not load-bearing for the benchmark construction, the robustness analysis, or the reported DRCT gains. The absence of DRCT results on CADC-SOT limits the external-real-world generalization claim, but that is a validity or scope limitation, not a circular derivation. No equation or argument in the paper reduces, by construction, to its own inputs. Hence no significant circularity is found.

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

The central benchmark and method depend on several user-chosen values that are not disclosed: five fog optical-range settings, five rainfall/snowfall rates, the CADC-SOT filtering thresholds, and DRCT augmentation and neighbor-count hyperparameters. These are not fitted to data in a statistical sense, but they are hand-chosen and affect every reported degradation number. The axioms are mostly standard evaluation assumptions plus the key fidelity assumption that synthetic weather simulation represents real weather for tracking. No new theoretical entities are introduced.

free parameters (5)
  • Fog optical range levels (5 values) = not reported
    Section III-B states the meteorological optical range is set at five discrete values, but the actual values are not given. These determine the fog corruption severity in KITTI-A and nuScenes-A.
  • Rainfall and snowfall rates (5 levels each) = not reported
    Section III-B uses the LISA simulation with rainfall and snowfall rates, but the specific rates for the five levels are not disclosed, so the synthetic rain and snow sets cannot be reproduced.
  • CADC-SOT filtering thresholds = 10 target points; subsequence length > 4
    Section III-C removes frames with fewer than 10 target points and sequences of length at most 4. These hand-chosen cutoffs shape the real-world benchmark composition and are not subjected to sensitivity analysis.
  • Domain randomization bounds Nmax and a = not reported
    Algorithm 1 depends on the maximum number of noise points Nmax and the jitter bound a for uniform perturbations. Neither value is reported, so the exact DRCT training augmentation is underspecified.
  • Ball Query neighbor count K = not reported
    Section V-C uses K neighboring points for local geometric contrastive learning. K is not specified, and no ablation over K is reported.
assumptions (5)
  • standard math Hausdorff distance is an appropriate scalar measure of template and target shape corruption in point clouds.
    Used in Section IV-A to quantify shape corruption; it is a standard metric but not necessarily robust to outliers.
  • domain assumption LISA and the fog simulation of [41] faithfully reproduce the effect of rain, snow, and fog on LiDAR point clouds across five severity levels.
    Section III-B relies on these prior simulation methods; their fidelity for tracking sequences is not validated in the paper.
  • domain assumption CADC's snowfall intensity labels and the paper's road-cover classification are reliable enough to split CADC-SOT into Light, Medium, Heavy, Extreme, Covered, and Non-covered subsets.
    Section III-C uses these labels without documenting the classification procedure or validating the splits.
  • domain assumption Trackers trained on clean data can be evaluated directly on corrupted sequences without adaptation, and clean validation performance is a valid baseline for computing degradation rates.
    Section IV evaluates pretrained trackers on corrupted data; this is standard but assumes the corruption does not introduce annotation biases or protocol artifacts.
  • ad hoc to paper The DRCT contrastive objective in Eq. (5), with a fixed K-neighborhood, aligns local features across clean and corrupted domains well enough to transfer robustness.
    The module is introduced by the paper; its convergence and transfer properties are supported only by the single KITTI-A evaluation, with no analysis on nuScenes-A or CADC-SOT.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions." pith.science (2026). https://pith.science/paper/GKD5EBFG

@misc{pith2026250107133,
  author       = {Pith},
  title        = {Pith review of: Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GKD5EBFG}},
  note         = {Machine review of arXiv:2501.07133}
}
read the original abstract

3D single object tracking (3DSOT) in LiDAR point clouds is a critical task for outdoor perception, enabling real-time perception of object location, orientation, and motion. Despite the impressive performance of current 3DSOT methods, evaluating them on clean datasets inadequately reflects their comprehensive performance, as the adverse weather conditions in real-world surroundings has not been considered. One of the main obstacles is the lack of adverse weather benchmarks for the evaluation of 3DSOT. To this end, this work proposes a challenging benchmark for LiDAR-based 3DSOT in adverse weather, which comprises two synthetic datasets (KITTI-A and nuScenes-A) and one real-world dataset (CADC-SOT) spanning three weather types: rain, fog, and snow. Based on this benchmark, five representative 3D trackers from different tracking frameworks conducted robustness evaluation, resulting in significant performance degradations. This prompts the question: What are the factors that cause current advanced methods to fail on such adverse weather samples? Consequently, we explore the impacts of adverse weather and answer the above question from three perspectives: 1) target distance; 2) template shape corruption; and 3) target shape corruption. Finally, based on domain randomization and contrastive learning, we designed a dual-branch tracking framework for adverse weather, named DRCT, achieving excellent performance in benchmarks.

Figures

Figures reproduced from arXiv: 2501.07133 by the authors.

Figure 1
Figure 1. Visualization of each weather type in our KITTI-A. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Visualization of each level snowy in our CADC-SOT. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 1
Figure 1. Specifically, to simulate foggy scenarios, we employed the methodology outlined in [41]. The parameter responsible for controlling the meteorological optical range was meticulously set at five discrete values. This enabled us to represent varying levels of fog with varying degrees of density, ranging from a light mist to a dense fog. For rainy and snowy conditions, we employed the LISA [40] simulation method for KIT… view at source ↗
Figures from the paper (5 more)
Figure 3
Figure 3. Figure 3: Analysis of the mean IOU deviation in KITTI-A. The same column represents the same weather and category, and the same row represents the same factor. There are three factors: Target Distance, the horizontal axis represents the target distance; Template Corruption, the …
Figure 4
Figure 4. Figure 4: The analysis in CADC-SOT, the horizontal axis represents the target [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: The overview of the proposed DRCT. DRCT consists of a primary branch (above) and an auxiliary branch (below), with a shared input of the point ˜ [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Visualization results demonstrate that we proposed method is competitive in adverse weather. The rows from top to bottom indicate rain, snow, and [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: The comparison with different contrastive learning structures. In this [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

50 extracted references · 39 canonical work pages

  1. [1]

    Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

    C. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in NIPS, 2017

    work page 2017

  2. [2]

    P2b: Point-to- box network for 3d object tracking in point clouds,

    H. Qi, C. Feng, Z. CAO, F. Zhao, and Y . Xiao, “P2b: Point-to- box network for 3d object tracking in point clouds,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 6328–6337, 2020

    work page 2020

  3. [3]

    3d siamese voxel-to- bev tracker for sparse point clouds,

    L. Hui, L. Wang, M. Cheng, J. Xie, and J. Yang, “3d siamese voxel-to- bev tracker for sparse point clouds,” in NeurIPS, 2021

    work page 2021

  4. [4]

    Pttr: Relational 3d point cloud object tracking with transformer,

    C. Zhou, Z. Luo, Y . Luo, T. Liu, L. Pan, Z. Cai, H. Zhao, and S. Lu, “Pttr: Relational 3d point cloud object tracking with transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), 2022, pp. 8531–8540

    work page 2022

  5. [5]

    Osp2b: One- stage point-to-box network for 3d siamese tracking,

    J. Nie, Z. He, Y . Yang, Z. Bao, M. Gao, and J. Zhang, “Osp2b: One- stage point-to-box network for 3d siamese tracking,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, E. Elkind, Ed. International Joint Conferences on Artificial Intelligence Organization, 8 2023, pp. 1285–1293, main Track. [Online]...

    work page doi:10.24963/ijcai.2023/143 2023

  6. [6]

    Glt-t: Global-local transformer voting for 3d single object tracking in point clouds,

    J. Nie, Z. He, Y . Yang, M. Gao, and J. Zhang, “Glt-t: Global-local transformer voting for 3d single object tracking in point clouds,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1957–1965

    work page 2023

  7. [7]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

    2017

  8. [8]

    3d siamese transformer network for single object tracking on point clouds,

    L. Hui, L. Wang, L. Tang, K. Lan, J. Xie, and J. Yang, “3d siamese transformer network for single object tracking on point clouds,” in European Conference on Computer Vision . Springer, 2022, pp. 293– 310. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 13

    work page 2022

Show all 50 references
  1. [9]

    Ost: Efficient one- stream network for 3d single object tracking in point clouds,

    X. Zhao, Y . Han, S. Tian, J. Liu, and X. Liu, “Ost: Efficient one- stream network for 3d single object tracking in point clouds,” IEEE Transactions on Multimedia , pp. 1–13, 2023

    2023

  2. [10]

    Dgcnn: A convolutional neural network over large-scale labeled graphs,

    A. V . Phan, M. Le Nguyen, Y . L. H. Nguyen, and L. T. Bui, “Dgcnn: A convolutional neural network over large-scale labeled graphs,” Neural Networks, vol. 108, pp. 533–543, 2018

    2018

  3. [11]

    Mbptrack: Improving 3d point cloud tracking with memory networks and box priors,

    T. Xu, Y . Guo, Y . Lai, and S. Zhang, “Mbptrack: Improving 3d point cloud tracking with memory networks and box priors,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. 9911–9920

    2023

  4. [12]

    Cxtrack: Improving 3d point cloud tracking with contextual information,

    T.-X. Xu, Y .-C. Guo, Y .-K. Lai, and S.-H. Zhang, “Cxtrack: Improving 3d point cloud tracking with contextual information,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 1084–1093

    2023

  5. [13]

    Beyond 3d siamese tracking: A motion-centric paradigm for 3d single object tracking in point clouds,

    C. Zheng, X. Yan, H. Zhang, B. Wang, S.-H. Cheng, S. Cui, and Z. Li, “Beyond 3d siamese tracking: A motion-centric paradigm for 3d single object tracking in point clouds,” ArXiv, vol. abs/2203.01730, 2022

    2022 arXiv

  6. [14]

    Learning the incremental warp for 3d vehicle tracking in lidar point clouds,

    S. Tian, X. Liu, M. Liu, Y . Bian, J. Gao, and B. Yin, “Learning the incremental warp for 3d vehicle tracking in lidar point clouds,” Remote Sensing, vol. 13, no. 14, p. 2770, 2021

    2021

  7. [15]

    Point cloud registration-driven robust feature matching for 3-d siamese object tracking,

    H. Jiang, K. Lan, L. Hui, G. Li, J. Xie, S. Gao, and J. Yang, “Point cloud registration-driven robust feature matching for 3-d siamese object tracking,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–11, 2023

    2023

  8. [16]

    Benchmarking neural network ro- bustness to common corruptions and perturbations,

    D. Hendrycks and T. Dietterich, “Benchmarking neural network ro- bustness to common corruptions and perturbations,” in International Conference on Learning Representations , 2018

    2018

  9. [17]

    Exploring implicit domain-invariant features for domain adaptive object detection,

    Q. Lang, L. Zhang, W. Shi, W. Chen, and S. Pu, “Exploring implicit domain-invariant features for domain adaptive object detection,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 4, pp. 1816–1826, 2023

    2023

  10. [18]

    Dynamics-aware adversarial attack of adaptive neural networks,

    A. Tao, Y . Duan, Y . Wang, J. Lu, and J. Zhou, “Dynamics-aware adversarial attack of adaptive neural networks,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 7, pp. 5505– 5518, 2024

    2024

  11. [19]

    Only once attack: Fooling the tracker with adversarial template,

    Z. Zhou, Y . Sun, Q. Sun, C. Li, and Z. Ren, “Only once attack: Fooling the tracker with adversarial template,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 7, pp. 3173–3184, 2023

    2023

  12. [20]

    Conda: Unsupervised domain adaptation for lidar segmentation via regularized domain concatenation,

    L. Kong, N. Quader, and V . E. Liong, “Conda: Unsupervised domain adaptation for lidar segmentation via regularized domain concatenation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 9338–9345

    2023

  13. [21]

    Revisiting open world object detection,

    X. Zhao, Y . Ma, D. Wang, Y . Shen, Y . Qiao, and X. Liu, “Revisiting open world object detection,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 5, pp. 3496–3509, 2024

    2024

  14. [22]

    Consensus synergizes with memory: A simple approach for anomaly segmentation in urban scenes,

    J. Cen, Z. Jiang, L. Xie, D. Jiang, W. Shen, and Q. Tian, “Consensus synergizes with memory: A simple approach for anomaly segmentation in urban scenes,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 2, pp. 1086–1097, 2024

    2024

  15. [23]

    Toward class-agnostic tracking using feature decorrelation in point clouds,

    S. Tian, J. Liu, and X. Liu, “Toward class-agnostic tracking using feature decorrelation in point clouds,” IEEE Transactions on Image Processing, vol. 33, pp. 682–695, 2024

    2024

  16. [24]

    Seeing through fog without seeing fog: Deep multi- modal sensor fusion in unseen adverse weather,

    M. Bijelic, T. Gruber, F. Mannan, F. Kraus, W. Ritter, K. Dietmayer, and F. Heide, “Seeing through fog without seeing fog: Deep multi- modal sensor fusion in unseen adverse weather,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, p...

    2020

  17. [25]

    Canadian adverse driving conditions dataset,

    M. Pitropov, D. E. Garcia, J. Rebello, M. Smart, C. Wang, K. Czarnecki, and S. Waslander, “Canadian adverse driving conditions dataset,” The International Journal of Robotics Research , vol. 40, no. 4-5, pp. 681– 690, 2021

    2021

  18. [26]

    Ithaca365: Dataset and driving perception under repeated and challenging weather conditions,

    C. A. Diaz-Ruiz, Y . Xia, Y . You, J. Nino, J. Chen, J. Monica, X. Chen, K. Luo, Y . Wang, M. Emond et al. , “Ithaca365: Dataset and driving perception under repeated and challenging weather conditions,” in Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Patter...

    2022

  19. [27]

    Are we ready for autonomous driving? the kitti vision benchmark suite,

    A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” 2012 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 3354–3361, 2012

    2012

  20. [28]

    nuscenes: A multi- modal dataset for autonomous driving,

    H. Caesar, V . Bankiti, A. H. Lang, S. V ora, V . E. Liong, Q. Xu, A. Krishnan, Y . Pan, G. Baldan, and O. Beijbom, “nuscenes: A multi- modal dataset for autonomous driving,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 11 618–11 628, 2020

    2020

  21. [29]

    Box- aware feature enhancement for single object tracking on point clouds,

    C. Zheng, X. Yan, J. Gao, W. Zhao, W. Zhang, Z. Li, and S. Cui, “Box- aware feature enhancement for single object tracking on point clouds,” 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , pp. 13 179–13 188, 2021

    2021

  22. [30]

    3d object tracking with transformer,

    Y . Cui, Z. Fang, J. Shan, Z. Gu, and S. Zhou, “3d object tracking with transformer,” in 32nd British Machine Vision Conference 2021, BMVC 2021, Online, November 22-25, 2021 . BMV A Press, 2021, p. 317

    2021

  23. [31]

    Ptt: Point-track-transformer module for 3d single object tracking in point clouds,

    J. Shan, S. Zhou, Z. Fang, and Y . Cui, “Ptt: Point-track-transformer module for 3d single object tracking in point clouds,” 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 1310–1316, 2021

    2021

  24. [32]

    Deep supervised descent method with multiple seeds generation for 3-d tracking in point cloud,

    S. Tian, B. Liu, H. Tan, J. Liu, M. Liu, and X. Liu, “Deep supervised descent method with multiple seeds generation for 3-d tracking in point cloud,” IEEE Transactions on Industrial Informatics , vol. 18, pp. 5077– 5086, 2022

    2022

  25. [33]

    Leveraging shape completion for 3d siamese tracking,

    S. Giancola, J. Zarzar, and B. Ghanem, “Leveraging shape completion for 3d siamese tracking,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 1359–1368, 2019

    2019

  26. [34]

    Benchmarking the robustness of lidar semantic segmentation models,

    X. Yan, C. Zheng, Z. Li, S. Cui, and D. Dai, “Benchmarking the robustness of lidar semantic segmentation models,” arXiv preprint arXiv:2301.00970, 2023

    2023 arXiv

  27. [35]

    3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds,

    A. Xiao, J. Huang, W. Xuan, R. Ren, K. Liu, D. Guan, A. El Saddik, S. Lu, and E. P. Xing, “3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- t...

    2023

  28. [36]

    Robo3d: Towards robust and reliable 3d perception against corruptions,

    L. Kong, Y . Liu, X. Li, R. Chen, W. Zhang, J. Ren, L. Pan, K. Chen, and Z. Liu, “Robo3d: Towards robust and reliable 3d perception against corruptions,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 994–20 006

    2023

  29. [37]

    Benchmarking robustness of 3d object detection to common corruptions,

    Y . Dong, C. Kang, J. Zhang, Z. Zhu, Y . Wang, X. Yang, H. Su, X. Wei, and J. Zhu, “Benchmarking robustness of 3d object detection to common corruptions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1022–1032

    2023

  30. [38]

    Transfer learning from synthetic to real lidar point cloud for semantic segmentation,

    A. Xiao, J. Huang, D. Guan, F. Zhan, and S. Lu, “Transfer learning from synthetic to real lidar point cloud for semantic segmentation,” Proceedings of the AAAI Conference on Artificial Intelligence, p. 2795–2803, Jul 2022. [Online]. Available: http: //dx.doi.org/10.1609/aaai.v...

    2022 doi

  31. [39]

    xmuda: Cross-modal unsupervised domain adaptation for 3d semantic segmentation,

    M. Jaritz, T.-H. Vu, R. de Charette, E. Wirbel, and P. Perez, “xmuda: Cross-modal unsupervised domain adaptation for 3d semantic segmentation,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2020. [Online]. Available: http://dx.doi.org/10.1...

    2020

  32. [40]

    Lidar snowfall simulation for robust 3d object detection,

    M. Hahner, C. Sakaridis, M. Bijelic, F. Heide, F. Yu, D. Dai, and L. Van Gool, “Lidar snowfall simulation for robust 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16 364–16 374

    2022

  33. [41]

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

    M. Hahner, C. Sakaridis, D. Dai, and L. 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, 2021, pp. 15 283–15 292

    2021

  34. [42]

    Invisible for both camera and lidar: Security of multi- sensor fusion based perception in autonomous driving under physical- world attacks,

    Y . Cao, N. Wang, C. Xiao, D. Yang, J. Fang, R. Yang, Q. A. Chen, M. Liu, and B. Li, “Invisible for both camera and lidar: Security of multi- sensor fusion based perception in autonomous driving under physical- world attacks,” in 2021 IEEE Symposium on Security and Privacy (SP...

    2021

  35. [43]

    On the real-world adversarial robustness of real-time semantic segmentation models for autonomous driving,

    G. Rossolini, F. Nesti, G. D’Amico, S. Nair, A. Biondi, and G. But- tazzo, “On the real-world adversarial robustness of real-time semantic segmentation models for autonomous driving,” IEEE Transactions on Neural Networks and Learning Systems , 2023

    2023

  36. [44]

    De-noising of lidar point clouds corrupted by snowfall,

    N. Charron, S. Phillips, and S. L. Waslander, “De-noising of lidar point clouds corrupted by snowfall,” 2018 15th Conference on Computer and Robot Vision (CRV) , pp. 254–261, 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:57760951

    2018

  37. [45]

    Pointfilternet: A filtering network for point cloud denoising,

    X. Wang, X. Fan, and D. Zhao, “Pointfilternet: A filtering network for point cloud denoising,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 3, pp. 1276–1290, 2023

    2023

  38. [46]

    Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather,

    M. Hahner, C. Sakaridis, D. Dai, and L. Van Gool, “Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather,” in IEEE International Conference on Computer Vision (ICCV) , 2021

    2021

  39. [47]

    LiDAR Snowfall Simulation for Robust 3D Object Detection,

    M. Hahner, C. Sakaridis, M. Bijelic, F. Heide, F. Yu, D. Dai, and L. Van Gool, “LiDAR Snowfall Simulation for Robust 3D Object Detection,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

    2022

  40. [48]

    Rethinking data augmentation for robust lidar semantic segmentation in adverse weather,

    J. Park, K. Kim, and H. Shim, “Rethinking data augmentation for robust lidar semantic segmentation in adverse weather,” arXiv preprint arXiv:2407.02286, 2024. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 14

    2024 arXiv

  41. [49]

    Unimix: Towards do- main adaptive and generalizable lidar semantic segmentation in adverse weather,

    H. Zhao, J. Zhang, Z. Chen, S. Zhao, and D. Tao, “Unimix: Towards do- main adaptive and generalizable lidar semantic segmentation in adverse weather,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 14 781–14 791

    2024

  42. [50]

    Domain randomization for transferring deep neural networks from sim- ulation to the real world,

    J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from sim- ulation to the real world,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2017, pp. 23–30

    2017

Pith tools

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