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REVIEW 4 major objections 6 minor 24 references

Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper introduces SynDRA-BBox, a synthetic railway dataset for 2D/3D detection, and shows tuned domain adaptation with it plus Waymo closes up to 176.50% of the source-to-oracle gap for OSDaR23 pedestrians.

desk verdict Useful public synthetic railway detection dataset plus a sensible SSDA3D recipe, but the paper must document target splits and label usage before its headline numbers can be checked. read the letter →

arxiv 2507.16413 v1 pith:UKCP7DAR submitted 2025-07-22 cs.CV cs.ET

Xavier Diaz , Gianluca D'Amico , Raul Dominguez-Sanchez , Federico Nesti , Max Ronecker , Giorgio Buttazzo This is my paper · ORCID
classification cs.CVcs.ET
keywords syntheticdatasetrailwayperception3DobjectdetectiondomainadaptationLiDARpointcloudssim-to-realtransfersemi-supervisedlearningmulti-source
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper aims to show that the railway sector's shortage of annotated 3D perception data can be addressed by a purpose-built synthetic dataset combined with domain adaptation. It introduces SynDRA-BBox, a publicly released synthetic dataset with camera, depth, and LiDAR data plus 2D and 3D bounding boxes, which it claims is the first public synthetic railway dataset supporting both 2D and 3D object detection. It then adapts SSDA3D, a semi-supervised domain adaptation method originally made for automotive point clouds, to railway scenes by reversing the CutMix direction, anchoring pasted source crops to nearby target points, requiring crops to contain labels, and sampling multiple sources proportionally to their size. The quantitative claim is that combining SynDRA-BBox with the automotive Waymo dataset under this tuned pipeline closes up to 176.50% of the source-to-oracle gap for OSDaR23 pedestrian detection, in places beating a model trained with full supervision on the target data. A sympathetic reader would care because this offers both a public benchmark and evidence that multi-source adaptation can surpass an oracle in a data-scarce safety-critical domain.

What carries the argument

The engine of the argument is SSDA3D, a two-stage semi-supervised domain adaptation method, paired with the CenterPoint 3D detector as its backbone. Stage one, Inter-domain Point-CutMix, cuts a region from a source point cloud and pastes it into a target cloud to train on hybrid samples; stage two, Intra-domain Point-MixUp, interpolates pairs of target samples with pseudo-labels. The paper's railway-specific modifications are: pasting source crops into the target rather than the reverse, shifting each crop along the ground plane to the nearest target point so the hybrid cloud keeps the long narrow TEL-15 frustum shape, rejecting crops that contain no ground-truth box, and sampling from multiple sources with probability proportional to dataset size when both SynDRA-BBox and Waymo are used. The Closed Gap metric, defined as (AP_model - AP_source-only)/(AP_oracle - AP_source-only), is the measure that lets a value above 100% be read as beating the oracle.

What would settle it

Re-run the S-to-O and SW-to-O experiments with SynDRA-BBox point clouds corrupted by realistic range noise, added intensity, and dropout, or with pedestrian meshes replaced by higher-fidelity scans, and check whether Waymo's advantage over synthetic data shrinks or reverses; a reversal would show the current numbers are artifacts of simulator simplicity. A second check: evaluate the same pipeline on OSDaR23 without filtering to the TEL-15 sensor to see whether the 176.50% closed-gap result is robust to sensor alignment choices.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a well-tuned semi-supervised domain adaptation pipeline can transfer 3D LiDAR object detection into a railway target domain even when no large real railway training set exists, and that the best transfer comes not from domain-matched synthetic data alone but from combining synthetic railway structure with large-scale real automotive data. Concretely, the paper reports that Waymo-to-OSDaR23 adaptation outperforms SynDRA-BBox-to-OSDaR23 on most metrics, yet the combined SynDRA-BBox plus Waymo source achieves the highest pedestrian results, including a 176.50% Closed Gap for person AP 3D at 0.5 IoU, meaning the adapted model exceeds the fully supervised oracle on that measure. The paper also introduces SynDRA-BBox itself as a claimed first public synthetic railway dataset for 2D and 3D object detection, with seven level-crossing scenarios plus a station scenario, rendered with two cameras and two LiDAR configurations, and annotations that include objects with as few as one LiDAR return, though training retains only objects with at least five points.

Load-bearing premise

The load-bearing premise is that clean, intensity-free simulated LiDAR point clouds are a faithful proxy for real railway LiDAR and that filtering OSDaR23 to the TEL-15 sensor makes the two comparable; if simulated noise, object shapes, or scanning patterns diverge too much from real sensors, the reported sim-to-real gap and the Waymo-versus-synthetic ranking would measure simulator quality rather than properties of synthetic data.

Editorial extensions

If this is right

  • A public benchmark now exists for training and comparing 2D/3D railway detectors without needing access to rare real-world rail data.
  • Domain adaptation can be engineered to exceed full supervision on the target set, so closed-gap values above 100% should be interpreted as meaningful model improvements rather than metric artifacts.
  • For rail, real automotive data is a stronger single source than domain-matched synthetic data, implying that data diversity and object realism can matter more than scene similarity.
  • Combining a small domain-specific synthetic source with a large real source is better than either alone, at least for pedestrian detection, so multi-source adaptation is a viable recipe for data-scarce domains.
  • The paper's tuned SSDA3D variant, with reversed and anchored CutMix plus label-preserving crops, is the version that produces these gains; off-the-shelf SSDA3D is measurably worse on the same setup.

Reading between the lines

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

  • My inference: the paper's own diagnosis implies that adding an intensity channel, realistic sensor noise, and higher-fidelity pedestrian geometry to SynDRA-BBox would shrink the reported Waymo advantage; that is a testable extension, since the authors name noise and pedestrian fidelity as the suspected causes.
  • My inference: the size-aware multi-source sampling rule could generalise to any domain-adaptation setting where one source is small but highly relevant; the paper does not claim this generality.
  • My inference: because OSDaR23 was filtered to the TEL-15 sensor to match SynDRA-BBox, some of the reported gap-closure figures may reflect how well the two point-cloud geometries were aligned; an unfiltered multi-LiDAR evaluation would separate sensor-matching effects from true domain transfer.
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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

4 major / 6 minor

Summary. The paper introduces SynDRA-BBox, a public synthetic railway dataset built in Unreal Engine 5 that provides RGB, depth, LiDAR, and 2D/3D bounding-box annotations, and claims it is the first publicly available synthetic dataset supporting 2D and 3D object detection in the railway domain. The authors then adapt the SSDA3D semi-supervised domain adaptation method to the railway setting, using SynDRA-BBox and/or the Waymo Open Dataset as source domains and the real-world OSDaR23 railway dataset as the target. They report domain adaptation results in terms of AP and a Closed Gap metric, with headline numbers such as a 176.50% Closed Gap for pedestrian AP3D at 0.5 IoU in the multi-source setup, and they argue that combining synthetic railway data with real automotive data can outperform a fully supervised oracle. The paper also describes several modifications to SSDA3D, including a size-aware CutMix sampling strategy and a reordered cut-paste operation suited to railway scenes.

Significance. If the claims hold, the dataset is a potentially useful public resource for railway perception, and the experimental study provides evidence that road-to-rail and sim-to-real adaptation can be combined. The use of an external public benchmark (OSDaR23) is a strength, as is the public release of the dataset. The paper also addresses an underexplored problem and reports both quantitative and qualitative results. However, the significance is currently limited by missing experimental details: the exact amount of labeled OSDaR23 target data used in the semi-supervised adaptation is not stated, single runs are reported without variance, and at least some reported Closed Gap numbers are internally inconsistent with the table's own AP values. The novelty claim regarding the 'first' synthetic railway detection dataset also needs to be reconciled with the cited SARD work.

major comments (4)
  1. [§II-A and Contributions] The paper claims SynDRA-BBox is the first publicly available synthetic dataset supporting 2D and 3D object detection in the railway domain, but the related-work section cites SARD [20] with the title '3D object detection on synthetic point clouds for railway applications' and then states that SARD 'do[es] not provide annotated bounding boxes for point cloud-based object detection.' This characterization appears to contradict the cited reference's stated purpose. Please clarify what annotations SARD actually provides and why it does not count as a prior synthetic railway 3D detection dataset; if SARD does contain 3D bounding boxes, the novelty claim must be revised or carefully qualified.
  2. [§V-A and §V-D] The paper never specifies how many OSDaR23 frames are used as labeled target data in the SSDA3D training, nor whether the OSDaR23 validation split is used during adaptation. The sentence 'During training, we use 100% of each training set' is ambiguous for the S→O, W→O, and SW→O setups. Since SSDA3D is described as using both labeled and unlabeled target data, and since stage-two Point-MixUp operates on unlabeled target samples, the authors must state exactly which OSDaR23 frames are used for the supervised target loss, which frames are used for pseudo-label generation, and must confirm that the 160 test frames are excluded from both stages. Without this information, the Closed Gap results, including the claim of exceeding the oracle, could in principle be inflated by test or validation leakage.
  3. [§V-E, Table II, and Eq. (1)] There are internal inconsistencies in Table II. For the S→O MixUp row at Person 0.25 IoU, the reported AP3D is 54.66 and the AP3D Closed Gap is 102.37, but using Eq. (1) with the table's S-only AP3D of 0.64 and Oracle AP3D of 51.60 gives approximately 106.0. Similarly, for the S→O MixUp row at Car 0.7 IoU, AP3D 1.72 with S-only 0 and Oracle 20.73 gives 8.30, not the reported 7.72. In addition, for the multi-source SW→O rows, the paper does not state which AP_source-only value (S-only or W-only) is used in the Closed Gap denominator, which is essential for interpreting the 176.50% result. Please correct the table and explicitly define the baseline for each row. Because many comparisons in Table II differ by only one or two AP points, the authors should also report standard deviations over multiple seeds rather than single runs.
  4. [§V-A and §V-D] The preprocessing applied to each dataset is not fully specified. The paper states that SynDRA-BBox objects with fewer than five LiDAR points are filtered 'to ensure consistency with real-world datasets,' but it does not say whether the same five-point filter was applied to OSDaR23 and Waymo. Likewise, the detection range [0.0, -54.0, -3.0, 216.0, 54.0, 6.8] is given, but it is not stated whether all three datasets were cropped to this range uniformly. These details are needed to determine whether the sim-to-real comparison is fair and to reproduce the experiments.
minor comments (6)
  1. [Abstract] The sentence beginning 'To the best of our knowledge, is the first synthetic dataset...' is missing a subject; it should read 'it is the first synthetic dataset...'.
  2. [§V-A] The text says 'Figure I shows statistics about the employed datasets,' but the referenced object is Table I, not a figure.
  3. [Table I] The label counts for OSDaR23 are given as 12.669 and 73.421, while Waymo counts are given as 6.024k and 2.772k; the units for OSDaR23 are unclear and should be written consistently (e.g., 12,669 and 73,421, or 12.669k and 73.421k).
  4. [§II-B] The phrase 'as proved in Waymo-to-nuScenes adaptation' should be 'as demonstrated in the Waymo-to-nuScenes adaptation,' since the cited evidence is experimental rather than a mathematical proof.
  5. [§V-E] The discussion of why Waymo outperforms SynDRA-BBox is plausible, but it would be strengthened by an ablation or diagnostic experiment that, for example, adds noise to SynDRA-BBox LiDAR or uses improved pedestrian meshes; the paper already mentions these as future work, which is good.
  6. [§III-D] The sentence '3D bounding boxes are directly obtained from the UE5’s internal functions' would benefit from a short description of the coordinate frame and the JSON annotation format, even if full details are on the project website.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the headline Closed Gap numbers are measured from independently trained models against external baselines, not fitted or definitionally forced.

full rationale

The paper's central quantitative claim is the Closed Gap metric defined in Eq. (1) as (AP_model - AP_source-only)/(AP_oracle - AP_source-only). This is a measurement ratio over independently evaluated detectors, not a quantity that is fitted into the model or defined in terms of the result it is used to support. The source-only and oracle baselines are trained separately, and the adaptation models are trained with SSDA3D using the disclosed source/target splits; the resulting AP values are reported directly. SynDRA-BBox is a dataset contribution whose novelty claim is historical rather than derivational, and the self-citations to SynDRA [10] and TrainSim [6] serve as provenance for the simulator and the base dataset, not as the load-bearing justification for the experimental conclusions. The concern about possible test-set leakage during semi-supervised adaptation is a correctness or experimental-protocol issue, not a circularity: no equation in the paper reduces to its own input, no fitted parameter is renamed as a prediction, and no author-specific uniqueness theorem is invoked to force a choice. Thus no circular step can be exhibited with the required quote-and-reduction standard.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claims rest on synthetic fidelity, cross-dataset label compatibility, and several hand-set hyperparameters. None of the constants are fitted to the target benchmark in a formal sense, but the lack of error bars and code means the influence of these choices cannot be isolated.

free parameters (5)
  • CutMix probability = 30%
    CutMix is applied with probability 0.3 during training; chosen by the authors, not swept or justified against other values.
  • MixUp probability = 50%
    MixUp is applied with probability 0.5; inherited from original SSDA3D, no ablation for the railway setting.
  • Minimum LiDAR points per object = 5 points
    SynDRA-BBox annotations are filtered to objects with at least five LiDAR returns to match real datasets; threshold chosen by hand.
  • Intensity fill value for synthetic points = 1.0
    In the SW-to-O experiment, SynDRA-BBox points receive constant intensity 1 because synthetic data lack an intensity channel; this choice affects the multi-source model.
  • 3D detection range = [0.0, -54.0, -3.0, 216.0, 54.0, 6.8] m
    The detection range is fixed to match the TEL-15 frustum; it changes which objects are inside the evaluation region and is not derived from data statistics.
assumptions (4)
  • domain assumption UE5 synthetic LiDAR with TEL-15 and HDL-64 models is representative of real railway point clouds.
    Invoked in Section III-B and Section IV; if the simulated sensor noise, object geometry, or scanning pattern are unrealistic, the sim-to-real conclusions do not transfer.
  • domain assumption Car and Person labels in SynDRA-BBox, Waymo, and OSDaR23 refer to the same semantic classes.
    Section V-A selects only common classes; label mismatch across datasets would distort the adaptation comparison.
  • domain assumption Filtering OSDaR23 to the middle TEL-15 LiDAR yields a point cloud comparable to SynDRA TEL-15 synthetic scans.
    Section IV removes other LiDAR sensors to align fields of view; the comparability of the resulting scans is assumed.
  • ad hoc to paper SSDA3D uses some labeled target frames from OSDaR23, but the exact split is not stated.
    The method is described as semi-supervised, yet the paper never specifies how many target labels are used, which is a load-bearing setup detail.

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Cite this review

Pith. "Pith review of Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox." pith.science (2026). https://pith.science/paper/UKCP7DAR

@misc{pith2026250716413,
  author       = {Pith},
  title        = {Pith review of: Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UKCP7DAR}},
  note         = {Machine review of arXiv:2507.16413}
}
read the original abstract

In recent years, interest in automatic train operations has significantly increased. To enable advanced functionalities, robust vision-based algorithms are essential for perceiving and understanding the surrounding environment. However, the railway sector suffers from a lack of publicly available real-world annotated datasets, making it challenging to test and validate new perception solutions in this domain. To address this gap, we introduce SynDRA-BBox, a synthetic dataset designed to support object detection and other vision-based tasks in realistic railway scenarios. To the best of our knowledge, is the first synthetic dataset specifically tailored for 2D and 3D object detection in the railway domain, the dataset is publicly available at https://syndra.retis.santannapisa.it. In the presented evaluation, a state-of-the-art semi-supervised domain adaptation method, originally developed for automotive perception, is adapted to the railway context, enabling the transferability of synthetic data to 3D object detection. Experimental results demonstrate promising performance, highlighting the effectiveness of synthetic datasets and domain adaptation techniques in advancing perception capabilities for railway environments.

Figures

Figures reproduced from arXiv: 2507.16413 by the authors.

Figure 1
Figure 1. Synthetic point cloud sample from SynDRA-BBox. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Samples from SynDRA-BBox showing a semantically segmented image on the left, which includes the proper legend [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Example scenarios from SynDRA-BBox. The left [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Waymo (orange) and OSDaR23 (green) point clouds [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Oracle vs. SSDA3D with both SynDRA-BBox and [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 18 canonical work pages

  1. [20]

    3d object detection on synthetic point clouds for railway applications,

    M. Neri and F. Battisti, “3d object detection on synthetic point clouds for railway applications,” in 2022 10th European Workshop on Visual Information Processing (EUVIP) . IEEE, 2022, pp. 1–6

    work page 2022

  2. [1]

    IEC 62267: Railway Applications – Automated Urban Guided Trans- port (AUGT) – Safety Requirements , International Electrotechnical Commission (IEC) Std., 2009, accessed: 2025-04-29

    work page 2009

  3. [2]

    Carla: An open urban driving simulator,

    A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V . Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16

    2017

  4. [3]

    Nvidia drive sim,

    NVIDIA Corporation, “Nvidia drive sim,” https://developer.nvidia. com/drive/drive-sim, 2022

    work page 2022

  5. [4]

    Ssda3d: semi-supervised domain adaptation for 3d object detection from point cloud,

    Y . Wang, J. Yin, W. Li, P. Frossard, R. Yang, and J. Shen, “Ssda3d: semi-supervised domain adaptation for 3d object detection from point cloud,” in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances ...

    work page doi:10.1609/aaai.v37i3.25370 2023

  6. [5]

    Airsim: High-fidelity visual and physical simulation for autonomous vehicles,

    S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in Field and Service Robotics: Results of the 11th International Conference . Springer, 2018, pp. 621–635

    2018

  7. [6]

    Trainsim: A railway simulation framework for lidar and camera dataset generation,

    G. D’Amico, M. Marinoni, F. Nesti, G. Rossolini, G. Buttazzo, S. Sabina, and G. Lauro, “Trainsim: A railway simulation framework for lidar and camera dataset generation,” IEEE Transactions on In- telligent Transportation Systems , vol. 24, no. 12, pp. 15 006–15 017, 2023

    work page 2023

  8. [7]

    Scenario-based validation of automated train systems using a 3d virtual railway environment,

    J. A. I. de Gordoa, S. Garc ´ıa, I. Urbieta, N. Aranjuelo, M. Nieto, D. O. de Eribe et al. , “Scenario-based validation of automated train systems using a 3d virtual railway environment,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2023, pp. 5072–5077

    work page 2023

Show all 24 references
  1. [8]

    Conditional weighted ensemble of transferred models for camera based onboard pedestrian detection in railway driver support systems,

    T. Toprak, B. Belenlioglu, B. Aydın, C. Guzelis, and M. A. Selver, “Conditional weighted ensemble of transferred models for camera based onboard pedestrian detection in railway driver support systems,” IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5041– 5054, 2020

    2020

  2. [9]

    Enhancing safety in railway environments: Interpretable track and obstacle detection using on-board lidar,

    A. Iglesias, J. L. Apellaniz, N. Aranjuelo, P. Brandimarte, J. Irastorza, and M. Nieto, “Enhancing safety in railway environments: Interpretable track and obstacle detection using on-board lidar,” IEEE Transactions on Intelligent Transportation Systems (T-ITS) , 2024, under review

    2024

  3. [10]

    Syndra: Synthetic dataset for railway applications,

    G. D’Amico, F. Nesti, G. Rossolini, M. Marinoni, S. Sabina, and G. Buttazzo, “Syndra: Synthetic dataset for railway applications,” in 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025, pp. 3437–3446

    2025

  4. [11]

    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,” in 2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012, pp. 3354–3361

    2012

  5. [12]

    Semantickitti: A dataset for semantic scene understanding of lidar sequences,

    J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “Semantickitti: A dataset for semantic scene understanding of lidar sequences,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 9297–9307

    2019

  6. [13]

    nuscenes: A multimodal 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 multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631

    2020

  7. [14]

    Scalability in perception for autonomous driving: Waymo open dataset,

    P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V . Patnaik, P. Tsui, J. Guo, Y . Zhou, Y . Chai, B. Caine et al. , “Scalability in perception for autonomous driving: Waymo open dataset,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , ...

    2020

  8. [15]

    Railgo- erl24: G \

    R. Tagiew, I. Wunderlich, M. Sastuba, and S. Seitz, “Railgo- erl24: G \” orlitz rail test center cv dataset 2024,” arXiv preprint arXiv:2504.00204, 2025

    2024 arXiv

  9. [16]

    Railsem19: A dataset for semantic rail scene understand- ing,

    O. Zendel, M. Murschitz, M. Zeilinger, D. Steininger, S. Abbasi, and C. Beleznai, “Railsem19: A dataset for semantic rail scene understand- ing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019, pp. 0–0

    2019

  10. [17]

    An effective railway intrusion detection method using dynamic intrusion region and lightweight neural network,

    Z. Cao, Y . Qin, Z. Xie, Q. Liu, E. Zhang, Z. Wu, and Z. Yu, “An effective railway intrusion detection method using dynamic intrusion region and lightweight neural network,” Measurement, vol. 191, p. 110564, 2022

    2022

  11. [18]

    Railset: A unique dataset for railway anomaly detection,

    A. Zouaoui, A. Mahtani, M. A. Hadded, S. Ambellouis, J. Boonaert, and H. Wannous, “Railset: A unique dataset for railway anomaly detection,” in 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) . IEEE, 2022, pp. 1–6

    2022

  12. [19]

    Railenv-pasmvs: A perfectly accurate, synthetic, path-traced dataset featuring a virtual railway environment for multi-view stereopsis training and reconstruction applications,

    A. Broekman and P. J. Gr ¨abe, “Railenv-pasmvs: A perfectly accurate, synthetic, path-traced dataset featuring a virtual railway environment for multi-view stereopsis training and reconstruction applications,” Data in Brief , vol. 38, p. 107411, 2021

    2021

  13. [21]

    Osdar23: Open sensor data for rail 2023,

    R. Tagiew, P. Klasek, R. Tilly, M. K ¨oppel, P. Denzler, P. Neumaier, T. Klockau, M. Boekhoff, and K. Schwalbe, “Osdar23: Open sensor data for rail 2023,” in 2023 8th International Conference on Robotics and Automation Engineering (ICRAE) . IEEE, 2023, pp. 270–276

    2023

  14. [22]

    Center-based 3d object detection and tracking,

    T. Yin, X. Zhou, and P. Kr ¨ahenb¨uhl, “Center-based 3d object detection and tracking,” CVPR, 2021

    2021

  15. [23]

    St3d: Self-training for unsupervised domain adaptation on 3d object detection,

    J. Yang, S. Shi, Z. Wang, H. Li, and X. Qi, “St3d: Self-training for unsupervised domain adaptation on 3d object detection,” in Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 10 368–10 378

    2021

  16. [24]

    Openpcdet: An open-source toolbox for 3d object detec- tion from point clouds,

    O. D. Team, “Openpcdet: An open-source toolbox for 3d object detec- tion from point clouds,” https://github.com/open-mmlab/OpenPCDet, 2020

    2020

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