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REVIEW 3 major objections 5 minor 48 references

SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Radar-only detector gains 10.4 mAP points via knowledge distillation from a lidar-radar teacher.

desk verdict Solid radar-only detection paper with a genuinely semi-supervised cross-modal distillation setup; the unablated confidence threshold is the main thing to push on in review. read the letter →

arxiv 2412.14571 v1 pith:FJS45WPA submitted 2024-12-19 cs.CV cs.AIeess.IV

Ruoyu Xu , Zhiyu Xiang , Chenwei Zhang , Hanzhi Zhong , Xijun Zhao , Ruina Dang , Peng Xu , Tianyu Pu
show 1 more author
Eryun Liu
This is my paper · ORCID
classification cs.CVcs.AIeess.IV
keywords 4Dradar3Dobjectdetectionknowledgedistillationsemi-supervisedlearningcross-modalityautonomousdrivingpseudo-labeling
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 tries to show that a cheap, radar-only 3D object detector can be made nearly as accurate as expensive multi-sensor systems by distilling knowledge from a lidar-radar fusion teacher network. The central claim is that semi-supervised distillation from a bi-modality teacher, using the teacher's own confident outputs as pseudo-labels, transfers enough feature quality to a radar-only student to boost its mean average precision by 10.38 points over the same network trained with ground truth. If true, this matters because autonomous vehicles could rely on 4D radar alone for perception, keeping real-time speed and all-weather robustness without the cost and latency of lidar or camera fusion.

What carries the argument

The load-bearing mechanism is the combination of three distillation signals. An adaptive fusion module in the teacher produces a fused lidar-radar feature map by learning per-modality weights, with a dropout gate that randomly discards one modality during training. Two feature-distillation losses, LRFD (lidar-to-radar) and FRFD (fusion-to-radar), align the student's radar features to the teacher's lidar and fused features using adapter convolutions. A semi-supervised output distillation loss treats the teacher's thresholded predictions as pseudo-labels, replacing ground truth supervision entirely.

What would settle it

Train the same SCKD pipeline on a dataset where the teacher is deliberately degraded, such as a subset of scenes with heavy occlusions or sensor noise, and measure whether the student's accuracy tracks the teacher's errors. If the student fails to improve over baseline or reproduces the teacher's systematic mistakes, the SSOD pseudo-label supervision is the point of failure.

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Extended reading notes

Core claim

The paper proposes SCKD, a distillation framework in which a lidar-radar fusion teacher (a SECOND backbone with an adaptive fusion module that weights and concatenates the two modalities, plus random modality dropout) supervises a radar-only SECOND student. Two feature-distillation losses, LRFD and FRFD, map the student's radar feature into the teacher's lidar and fused feature spaces through learned adapters, and a semi-supervised output distillation loss uses the teacher's detections with confidence above 0.1 as pseudo-labels. The student is trained entirely on teacher supervision, with no ground-truth labels, and at inference only the student runs. On the View-of-Delft dataset the student reaches 52.08 mAP versus 41.70 for the baseline SECOND, surpassing all prior radar-only methods and all radar-camera fusion methods except LXL, while running at 39.3 FPS. On ZJUODset, adding unlabeled data improves the moderate-level mAP by 5.12 points over the same baseline.

Load-bearing premise

The teacher's detection outputs, kept as pseudo-labels when their confidence exceeds 0.1, are good enough substitutes for human-annotated ground truth; if those pseudo-labels are too noisy, the student will absorb the teacher's errors.

Editorial extensions

If this is right

  • A radar-only detector can reach or exceed the accuracy of most lidar-camera or radar-camera fusion detectors while running an order of magnitude faster.
  • The student network can be trained without any labeled data beyond what the teacher needs, so large unlabeled corpora such as raw driving logs become usable for improving radar detection.
  • Feature-level distillation from a bi-modality teacher is more effective than from a single-modality teacher because the fused feature space is closer to the student's radar-only space.
  • Using two separate adapters in FRFD rather than a single upscaling convolution preserves the fused feature structure and improves transfer.

Reading between the lines

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

  • If SCKD's gains hold across sensors and weather conditions, radar-based perception could become a primary modality for low-cost autonomy, since the distillation pipeline transfers semantic richness without adding sensor cost at deployment.
  • The success of SSOD suggests that a well-trained teacher's noisy but high-recall outputs at a low confidence threshold may serve as a more effective training signal than ground-truth boxes for the student, because teacher false positives carry contextual information that manual labels omit.
  • A testable extension is to apply SCKD to other backbone architectures and radar datasets, such as TJ4DRadSet or RADIATE, to check whether the gain is specific to SECOND and VoD or generalizes across architectures and domains.
  • A possible failure mode to probe is distribution shift in unlabeled data: if the unlabeled pool comes from different weather or traffic density than the teacher's training set, the pseudo-labels may become systematically biased, and student performance on labeled validation splits would reveal this.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The manuscript proposes SCKD, a semi-supervised cross-modality knowledge distillation method for 4D radar 3D object detection. A Lidar-radar fusion teacher network, enhanced by an adaptive fusion module with random modality dropout, is used to train a radar-only SECOND student. Knowledge is transferred through two feature-distillation losses (LRFD and FRFD) and a semi-supervised output distillation loss (SSOD) in which teacher detections above a confidence threshold sigma replace ground-truth labels as student supervision. Experiments on VoD and ZJUODset report that the radar-only student improves mAP by 10.38 points over the SECOND baseline on VoD, surpasses previous radar-only methods, and approaches or exceeds most radar-camera fusion methods while running at 39.3 FPS.

Significance. If the empirical claims are robust, SCKD is a useful contribution: it demonstrates that a radar-only detector can benefit from a Lidar-radar teacher at no inference cost, and it provides a semi-supervised mechanism for exploiting unlabeled radar data. The public code release, evaluation on two datasets, and the teacher-design analysis (Table 5) are strengths. The main limitations are that all numbers come from single runs, the reported margin over the strongest radar-only competitor (SMURF, Table 1) is only 1.11 mAP, and the key pseudo-label threshold sigma is not ablated. These issues do not invalidate the idea, but they need to be addressed before the state-of-the-art and 'SSOD > GT' claims can be considered established.

major comments (3)
  1. [SSOD: Semi-Supervised Output Distillation, Eq. (13), Table 3] The central comparison in the ablation, row (b) vs row (a), is used to claim that SSOD alone is more effective than ground-truth supervision, yet this comparison is only performed at sigma=0.1. The threshold is not ablated, and the precision/recall of the teacher's pseudo-labels is not reported. Because sigma defines the entire supervision signal for the student in the main VoD configuration, a sensitivity analysis over sigma (and ideally pseudo-label quality statistics) is required to show that the reported 10.38-point gain is not an artifact of this particular threshold.
  2. [Tables 1-5, experimental methodology] Across Tables 1-5, all results are single-run; no standard deviations or number of seeds are reported. This is particularly important for the headline comparison in Table 1, where the margin over SMURF is 1.11 mAP in the full area and 2.08 mAP in the corridor; these margins may be within run-to-run variation for 3D detectors. Please report mean +/- std over at least three seeds for the main comparisons and the ablations, or otherwise justify the stability of the improvements.
  3. [Table 3, ablation study] Table 3's formatting makes the ablation hard to verify. Several rows appear to contain the same set of checkmarks under the printed column headers, and the text's reference to rows '(e) and (d)' does not match the reader's parsing of which components are active. The incremental claims (SSOD vs GT, one-adapter vs two-adapter FRFD, LRFD contribution) need a table with explicit per-row component indicators and the exact configurations used; otherwise the ablation does not support the decomposition claimed in the text.
minor comments (5)
  1. [Abstract and Section 'Main Results'] The abstract and main text should say 'percentage points' rather than '%' for mAP improvements: 10.38% and 5.12% are relative changes only if the denominator is specified; the reported numbers are absolute mAP differences.
  2. [Author affiliations] The author affiliation contains a typo: 'Institude' should be 'Institute'.
  3. [Equations (6)-(7)] Equations (6)-(7): 'index function' should be 'indicator function'.
  4. [Eq. (13)-(14)] The notation in Eq. (13) could be clarified: D_T is introduced as a set of selected targets, but then used as a mask in Eq. (14); please define it as a binary mask explicitly.
  5. [Table 4] In Table 4, the row labeled SCKD uses a teacher trained on 1/4 labeled data and a student on 1/4 unlabeled data, whereas SCKD+ uses full unlabeled data; the text should state this explicitly to avoid confusion with the main SCKD configuration.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported gains are empirical measurements on held-out validation data, not derivations from fitted inputs or self-citation chains.

full rationale

The paper's central claims are empirical: a radar-only student trained with SCKD yields a measured mAP improvement over baselines and state-of-the-art methods on VoD and ZJUODset. The student is supervised by the teacher's pseudo-labels (Eq. 13) and by the distillation losses (Eqs. 8, 12, 14), while evaluation is performed on a separate validation split against ground truth, so there is no label leakage and no fitted parameter is renamed as a prediction. Hyperparameters such as sigma=0.1, alpha, and beta are fixed values, not fitted to the test set; the absence of a sigma sensitivity study is a robustness concern, not a circular derivation. The only self-citation is the ZJUODset dataset (Xu et al. 2023), used as a benchmark; it does not supply a load-bearing theoretical premise. No equation in the paper reduces to its own input by construction, and no uniqueness theorem or ansatz is imported via self-citation to force the result. Therefore the derivation chain is self-contained for the purposes of circularity analysis.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The central claim is an empirical performance gain, so the ledger lists hand-chosen hyperparameters and domain assumptions about distillation and pseudo-label quality. No new physical or conceptual entities are introduced.

free parameters (5)
  • loss weight alpha = 3e-4
    Balances LRFD loss in the total loss (Eq. 15); chosen by hand, no sensitivity analysis.
  • loss weight beta = 3e-4
    Balances FRFD loss in the total loss (Eq. 15); chosen by hand.
  • pseudo-label confidence threshold sigma = 0.1
    Retention threshold for teacher targets in SSOD (Eq. 13); set without reported tuning.
  • modality dropout probability Pdrop = 0.2
    Set to 0.2 for the teacher fusion dropout; only two values are discussed.
  • Lidar dropout probability PL = 0.2
    Set to 0.2 in experiments; part of the random dropout gate in the adaptive fusion module.
assumptions (3)
  • domain assumption Knowledge distillation from a stronger multimodal teacher to a weaker unimodal student transfers useful information
    Core premise of the method; supported by prior KD literature.
  • domain assumption Pseudo-labels from a teacher trained on labeled data are a valid training signal for a student on the same data without ground truth
    Underlies SSOD; the confidence threshold is 0.1, which admits many false positives.
  • domain assumption The VoD and ZJUODset benchmarks are representative for 4D radar detection and the published baseline numbers are comparable
    All comparisons use numbers from prior papers without re-running under identical settings.

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Cite this review

Pith. "Pith review of SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection." pith.science (2026). https://pith.science/paper/FJS45WPA

@misc{pith2026241214571,
  author       = {Pith},
  title        = {Pith review of: SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FJS45WPA}},
  note         = {Machine review of arXiv:2412.14571}
}
read the original abstract

3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor is able to acquire 3D point clouds similar to Lidar while maintaining robust measurements under adverse weather. However, due to the high sparsity and noise associated with the radar point clouds, the performance of the existing methods is still much lower than expected. In this paper, we propose a novel Semi-supervised Cross-modality Knowledge Distillation (SCKD) method for 4D radar-based 3D object detection. It characterizes the capability of learning the feature from a Lidar-radar-fused teacher network with semi-supervised distillation. We first propose an adaptive fusion module in the teacher network to boost its performance. Then, two feature distillation modules are designed to facilitate the cross-modality knowledge transfer. Finally, a semi-supervised output distillation is proposed to increase the effectiveness and flexibility of the distillation framework. With the same network structure, our radar-only student trained by SCKD boosts the mAP by 10.38% over the baseline and outperforms the state-of-the-art works on the VoD dataset. The experiment on ZJUODset also shows 5.12% mAP improvements on the moderate difficulty level over the baseline when extra unlabeled data are available. Code is available at https://github.com/Ruoyu-Xu/SCKD.

Figures

Figures reproduced from arXiv: 2412.14571 by the authors.

Figure 1
Figure 1. Comparison of the current mainstream cross [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of our SCKD Framework. The solid and dashed lines represent the data flow and calculation of the distil [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The structure of the Adaptive Fusion module. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Qualitative results on the VoD dataset. (a) shows [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

48 extracted references · 34 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Bang, G.; Choi, K.; Kim, J.; Kum, D.; and Choi, J. W. 2024. RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 15491--15500

    work page 2024

  4. [4]

    H.; Vora, S.; Liong, V

    Caesar, H.; Bankiti, V.; Lang, A. H.; Vora, S.; Liong, V. E.; Xu, Q.; Krishnan, A.; Pan, Y.; Baldan, G.; and Beijbom, O. 2020. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 11621--11631

    2020

  5. [5]

    Chen, X.; Cao, Q.; Zhong, Y.; Zhang, J.; Gao, S.; and Tao, D. 2022 a . Dearkd: Data-efficient early knowledge distillation for vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12052--12062

    work page 2022

  6. [6]

    Chen, X.; Zhang, T.; Wang, Y.; Wang, Y.; and Zhao, H. 2023. Futr3d: A unified sensor fusion framework for 3d detection. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 172--181

    2023

  7. [7]

    Chen, Y.; Wang, S.; Liu, J.; Xu, X.; de Hoog, F.; and Huang, Z. 2022 b . Improved feature distillation via projector ensemble. Advances in Neural Information Processing Systems, 35: 12084--12095

    work page 2022

  8. [8]

    Chen, Z.; Li, Z.; Zhang, S.; Fang, L.; Jiang, Q.; and Zhao, F. 2022 c . Bevdistill: Cross-modal bev distillation for multi-view 3d object detection. arXiv preprint arXiv:2211.09386

    arXiv 2022

Show all 48 references
  1. [9]

    Chong, Z.; Ma, X.; Zhang, H.; Yue, Y.; Li, H.; Wang, Z.; and Ouyang, W. 2022. Monodistill: Learning spatial features for monocular 3d object detection. arXiv preprint arXiv:2201.10830

    2022 arXiv

  2. [10]

    Contributors, M. 2020. MMDetection3D: OpenMMLab next-generation platform for general 3D object detection. https://github.com/open-mmlab/mmdetection3d

    2020

  3. [11]

    Deng, J.; Chan, G.; Zhong, H.; and Lu, C. X. 2023. See Beyond Seeing: Robust 3D Object Detection from Point Clouds via Cross-Modal Hallucination. arXiv preprint arXiv:2309.17336

    2023 arXiv

  4. [12]

    Du, L.; Ye, X.; Tan, X.; Feng, J.; Xu, Z.; Ding, E.; and Wen, S. 2020. Associate-3Ddet: Perceptual-to-conceptual association for 3D point cloud object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 13329--13338

    2020

  5. [13]

    Hwang, J.-J.; Kretzschmar, H.; Manela, J.; Rafferty, S.; Armstrong-Crews, N.; Chen, T.; and Anguelov, D. 2022. Cramnet: Camera-radar fusion with ray-constrained cross-attention for robust 3d object detection. In European conference on computer vision, 388--405. Springer

    2022

  6. [14]

    Jiao, Y.; Jie, Z.; Chen, S.; Chen, J.; Ma, L.; and Jiang, Y.-G. 2023. Msmdfusion: Fusing lidar and camera at multiple scales with multi-depth seeds for 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 21643--21652

    2023

  7. [15]

    W.; and Kum, D

    Kim, Y.; Kim, S.; Choi, J. W.; and Kum, D. 2023 a . Craft: Camera-radar 3d object detection with spatio-contextual fusion transformer. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, 1160--1168

    2023

  8. [16]

    W.; and Kum, D

    Kim, Y.; Shin, J.; Kim, S.; Lee, I.-J.; Choi, J. W.; and Kum, D. 2023 b . Crn: Camera radar net for accurate, robust, efficient 3d perception. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 17615--17626

    2023

  9. [17]

    H.; Vora, S.; Caesar, H.; Zhou, L.; Yang, J.; and Beijbom, O

    Lang, A. H.; Vora, S.; Caesar, H.; Zhou, L.; Yang, J.; and Beijbom, O. 2019. Pointpillars: Fast encoders for object detection from point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 12697--12705

    2019

  10. [18]

    Li, X.; Ma, T.; Hou, Y.; Shi, B.; Yang, Y.; Liu, Y.; Wu, X.; Chen, Q.; Li, Y.; Qiao, Y.; et al. 2023. Logonet: Towards accurate 3d object detection with local-to-global cross-modal fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 17...

    2023

  11. [19]

    Liang, T.; Xie, H.; Yu, K.; Xia, Z.; Lin, Z.; Wang, Y.; Tang, T.; Wang, B.; and Tang, Z. 2022. Bevfusion: A simple and robust lidar-camera fusion framework. Advances in Neural Information Processing Systems, 35: 10421--10434

    2022

  12. [20]

    Lin, Z.; Liu, Z.; Xia, Z.; Wang, X.; Wang, Y.; Qi, S.; Dong, Y.; Dong, N.; Zhang, L.; and Zhu, C. 2024. RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection. arXiv preprint arXiv:2403.16440

    2024 arXiv

  13. [21]

    Liu, J.; Zhao, Q.; Xiong, W.; Huang, T.; Han, Q.-L.; and Zhu, B. 2023. SMURF: Spatial multi-representation fusion for 3D object detection with 4D imaging radar. IEEE Transactions on Intelligent Vehicles

    2023

  14. [22]

    Nabati, R.; and Qi, H. 2021. Centerfusion: Center-based radar and camera fusion for 3d object detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 1527--1536

    2021

  15. [23]

    F.; and Gavrila, D

    Palffy, A.; Pool, E.; Baratam, S.; Kooij, J. F.; and Gavrila, D. M. 2022. Multi-class road user detection with 3+ 1D radar in the View-of-Delft dataset. IEEE Robotics and Automation Letters, 7(2): 4961--4968

    2022

  16. [24]

    R.; Su, H.; Mo, K.; and Guibas, L

    Qi, C. R.; Su, H.; Mo, K.; and Guibas, L. J. 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 652--660

    2017

  17. [25]

    Sheeny, M.; De Pellegrin, E.; Mukherjee, S.; Ahrabian, A.; Wang, S.; and Wallace, A. 2021. Radiate: A radar dataset for automotive perception in bad weather. In 2021 IEEE International Conference on Robotics and Automation (ICRA), 1--7. IEEE

    2021

  18. [26]

    Shi, S.; Wang, X.; and Li, H. 2019. Pointrcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 770--779

    2019

  19. [27]

    Team, O. D. 2020. OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds. https://github.com/open-mmlab/OpenPCDet

    2020

  20. [28]

    H.; Helou, B.; and Beijbom, O

    Vora, S.; Lang, A. H.; Helou, B.; and Beijbom, O. 2020. Pointpainting: Sequential fusion for 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 4604--4612

    2020

  21. [29]

    Wang, C.; Ma, C.; Zhu, M.; and Yang, X. 2021. Pointaugmenting: Cross-modal augmentation for 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11794--11803

    2021

  22. [30]

    Wang, L.; Zhang, X.; Li, J.; Xv, B.; Fu, R.; Chen, H.; Yang, L.; Jin, D.; and Zhao, L. 2022 a . Multi-modal and multi-scale fusion 3D object detection of 4D radar and LiDAR for autonomous driving. IEEE Transactions on Vehicular Technology

    2022

  23. [31]

    Wang, L.; Zhang, X.; Xv, B.; Zhang, J.; Fu, R.; Wang, X.; Zhu, L.; Ren, H.; Lu, P.; Li, J.; et al. 2022 b . InterFusion: Interaction-based 4D radar and LiDAR fusion for 3D object detection. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 122...

    2022

  24. [32]

    Wang, Z.; Li, D.; Luo, C.; Xie, C.; and Yang, X. 2023. Distillbev: Boosting multi-camera 3d object detection with cross-modal knowledge distillation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 8637--8646

    2023

  25. [33]

    Xiong, W.; Liu, J.; Huang, T.; Han, Q.-L.; Xia, Y.; and Zhu, B. 2023. LXL: LiDAR excluded lean 3D object detection with 4D imaging radar and camera fusion. IEEE Transactions on Intelligent Vehicles

    2023

  26. [34]

    Xu, B.; Zhang, X.; Wang, L.; Hu, X.; Li, Z.; Pan, S.; Li, J.; and Deng, Y. 2021. RPFA-Net: A 4D radar pillar feature attention network for 3D object detection. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 3061--3066. IEEE

    2021

  27. [35]

    Xu, R.; Xiang, Z.; Zhao, J.; Dang, R.; and Wu, Z. 2023. ZJUODset: a long range 3D object detection dataset with 4D radar

    2023

  28. [36]

    Yan, Q.; and Wang, Y. 2023. Mvfan: Multi-view feature assisted network for 4d radar object detection. In International Conference on Neural Information Processing, 493--511. Springer

    2023

  29. [37]

    Yan, Y.; Mao, Y.; and Li, B. 2018. Second: Sparsely embedded convolutional detection. Sensors, 18(10): 3337

    2018

  30. [38]

    Yang, J.; Shi, S.; Ding, R.; Wang, Z.; and Qi, X. 2022 a . Towards efficient 3d object detection with knowledge distillation. Advances in Neural Information Processing Systems, 35: 21300--21313

    2022

  31. [39]

    Yang, Z.; Li, Z.; Jiang, X.; Gong, Y.; Yuan, Z.; Zhao, D.; and Yuan, C. 2022 b . Focal and global knowledge distillation for detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4643--4652

    2022

  32. [40]

    Yang, Z.; Li, Z.; Shao, M.; Shi, D.; Yuan, Z.; and Yuan, C. 2022 c . Masked generative distillation. In European Conference on Computer Vision, 53--69. Springer

    2022

  33. [41]

    Yang, Z.; Sun, Y.; Liu, S.; and Jia, J. 2020. 3dssd: Point-based 3d single stage object detector. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 11040--11048

    2020

  34. [42]

    Yin, T.; Zhou, X.; and Krahenbuhl, P. 2021. Center-based 3d object detection and tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 11784--11793

    2021

  35. [43]

    Zheng, L.; Li, S.; Tan, B.; Yang, L.; Chen, S.; Huang, L.; Bai, J.; Zhu, X.; and Ma, Z. 2023. Rcfusion: Fusing 4d radar and camera with bird’s-eye view features for 3d object detection. IEEE Transactions on Instrumentation and Measurement

    2023

  36. [44]

    Zheng, L.; Ma, Z.; Zhu, X.; Tan, B.; Li, S.; Long, K.; Sun, W.; Chen, S.; Zhang, L.; Wan, M.; et al. 2022 a . TJ4DRadSet: A 4D radar dataset for autonomous driving. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 493--498. IEEE

    2022

  37. [45]

    Zheng, W.; Tang, W.; Jiang, L.; and Fu, C.-W. 2021. SE-SSD: Self-ensembling single-stage object detector from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 14494--14503

    2021

  38. [46]

    Zheng, Z.; Ye, R.; Wang, P.; Ren, D.; Zuo, W.; Hou, Q.; and Cheng, M.-M. 2022 b . Localization distillation for dense object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9407--9416

    2022

  39. [47]

    Zhou, S.; Liu, W.; Hu, C.; Zhou, S.; and Ma, C. 2023. UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5116--5125

    2023

  40. [48]

    Zhou, Y.; and Tuzel, O. 2018. Voxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, 4490--4499

    2018

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Reviewed August 11, 2026 · model on record in the stance chip above.