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REVIEW 5 major objections 5 minor 40 references

CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving

T0 review · 5 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper claims that a plug-in denoising module trained with a LiDAR-derived voxel mask can lift radar-only 3D car detection on the Dual-Radar dataset by over 34 percentage points, and into double digits for small objects.

desk verdict Plausible cross-modal denoising idea, but the reported numbers don't cross-check: Table I and the ablations differ by ~18 AP points for the same configuration, so the headline gain is not trustworthy as written. read the letter →

arxiv 2508.13485 v1 pith:ZZNX7YTS submitted 2025-08-19 cs.CV cs.AI

Fuyang Liu , Jilin Mei , Fangyuan Mao , Chen Min , Yan Xing , Yu Hu This is my paper · ORCID
classification cs.CVcs.AI
keywords 4DradarpointclouddenoisingLiDARsupervisioncross-modallearning3Dobjectdetectionvoxel-basedautonomousdrivingDual-Radardataset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that 4D radar detection can be materially improved by teaching a plug-in denoising module what noise looks like, using LiDAR as a teacher during training only. The proposed CORENet adds a hierarchical multi-scale denoising network (HMSD-Net) and a voxel-level mask loss computed by matching each radar voxel to LiDAR points within 0.5 m. Integrated with the CasA-V detector on the Dual-Radar dataset, it raises car 3D AP from 14.81% to 49.46% and BEV AP from 16.87% to 54.38%, with mean AP across car, cyclist, and pedestrian also improving over classical denoising baselines. If this holds, radar-only perception can absorb LiDAR-level supervision without needing LiDAR at inference, which matters for robust autonomous driving in weather where LiDAR degrades.

What carries the argument

The central object is the voxel-validity mask: a binary label per radar voxel, produced by a KDTree nearest-neighbor search that marks a radar voxel valid if any LiDAR point lies within 0.5 m (Eqs. 4–5). HMSD-Net predicts this mask from radar data alone, and the Smooth L1 voxel-mask loss (weight 50) pulls the network toward keeping LiDAR-consistent voxels while discarding sidelobe noise. The mask is the conduit through which LiDAR knowledge enters the radar-only detector at training time.

What would settle it

Train the identical CORENet on the Dual-Radar frames with the LiDAR-derived mask replaced by a mask shifted by 2 m in a random direction; if car 3D AP stays near 49%, the mask loss is not the source of the gain. Alternatively, evaluate the trained model on objects visible to radar but beyond LiDAR range, or in fog where LiDAR drops out: if detection on those objects collapses, the 0.5 m rule is discarding genuine radar returns.

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Extended reading notes

Core claim

The central claim is that cross-modal supervision — a binary mask derived from LiDAR proximity plus a denoising head appended to an existing voxel-based detector — lets a radar-only network suppress the sidelobe noise that dominates 4D radar point clouds. HMSD-Net combines point-topology features (HPNet) with sparse-convolution spatial features (SConvNet) and predicts a per-voxel validity score; the score is trained against the LiDAR-derived mask with Smooth L1 loss. At inference the mask generator and LiDAR are dropped. On the Dual-Radar test set, CasA-V + CORENet reaches 49.46% 3D AP and 54.38% BEV AP for cars, absolute gains of +34.64 and +37.51 points over CasA-V, and the Voxel R-CNN var

Load-bearing premise

The load-bearing premise is that a radar voxel is valid exactly when a LiDAR point sits within 0.5 m of it, and that the 9,966 selected Dual-Radar frames fairly represent noisy 4D radar; if the threshold mislabels genuine radar returns or the frame selection is skewed, the reported AP gains may not transfer.

Editorial extensions

If this is right

  • Radar-only detectors can benefit from LiDAR-quality supervision without any LiDAR input at inference, preserving cost and robustness advantages of radar.
  • The plug-in design means the same denoising module and mask loss can be attached to any voxel-based 3D detector, not just CasA-V and Voxel R-CNN.
  • Classical denoising filters (ROR, SOR) are outperformed by the learned denoiser, suggesting that noise in 4D radar is structured enough to be learned rather than removed by statistics alone.
  • Small-object detection (pedestrians and cyclists) improves more in relative terms than car detection, indicating the denoiser helps exactly the sparse, few-point objects that typically fail.
  • The cross-modal supervision could be extended to other tasks built on radar voxels, such as segmentation or tracking, by reusing the same mask target.

Reading between the lines

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

  • A testable extension not pursued in the paper is to replace the LiDAR-derived mask with a shifted or corrupted mask: if AP stays high, the mask loss is not the active ingredient; if AP collapses, the 0.5 m neighbor rule is doing the work.
  • The 0.5 m threshold ties the method's ceiling to LiDAR sampling density; on long-range or occluded objects where LiDAR returns are absent, the supervision would label genuine radar points as noise, so gains may shrink exactly where radar is most valuable.
  • The same denoising-by-mask idea could be trained with camera-derived depth or occupancy pseudo-labels, removing even the training-time LiDAR requirement, at the cost of a weaker teacher.
  • The paper reports gains on a selected subset of Dual-Radar frames without stating the selection criteria, so an independent evaluation on the full dataset would clarify how much of the improvement is due to the method rather than the sample.
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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

5 major / 5 minor

Summary. The manuscript proposes CORENet, a plug-and-play cross-modal denoising module for 4D radar voxel-based 3D object detection. During training, LiDAR points are used to generate binary supervision masks by thresholding nearest-neighbor distances (KDTree, tau = 0.5 m); the radar stream passes through a hierarchical multi-scale denoising network (HMSD-Net, consisting of HPNet and SConvNet) and a Noise Predictor whose output is compared with the mask via Smooth L1 loss, added to the detection losses. At inference, LiDAR is not needed. The method is evaluated on a selected subset of the Dual-Radar dataset (9966 frames), and Table I reports large gains when CORENet is integrated with CasA-V and Voxel R-CNN.

Significance. The inference-time radar-only property and the plug-and-play design are practically attractive, and the paper makes a reasonable step toward using LiDAR supervision to denoise 4D radar. The method's modularity and the inclusion of a code repository are strengths. However, the strength of the evidence is currently compromised by a large internal inconsistency between the test-table results and the ablation tables for the same configuration, by an unexplained dataset filtering procedure, and by the omission of the closest prior method (RadarDistill). The core idea is not entirely new, but the specific noise-mask formulation and hierarchical denoising architecture are reasonable contributions. If the inconsistencies are resolved and the results are reproducible, the paper would be a useful contribution to the radar perception community.

major comments (5)
  1. [Sec. IV-C vs. Sec. IV-D, Tables I and II-IV] The same configuration (CasA-V + CORENet, HPNet + SConvNet, KDTree distance 0.5 m, 1-layer Noise Predictor) reports Car 3D AP 49.46% and BEV AP 54.38% in Table I, but Car 3D AP 67.34% and BEV AP 71.23% in Table II (also rows in Tables III and IV). These values differ by roughly 18 points, with no stated difference in dataset split, IoU threshold, or evaluation protocol. The mean AP differs similarly (27.47% vs 21.37% 3D; 29.01% vs 23.14% BEV). This directly undermines the headline improvement claim of +34.64%/+37.51% over the CasA-V baseline, because a reader cannot determine which set of numbers constitutes the actual test result. The authors must clarify the split used for each table and either rerun the ablations on the test set or explain the discrepancy.
  2. [Sec. IV-A] The dataset description states 'After effective selection, the experimental dataset comprises 9966 frames' but gives no criteria for this selection. The Dual-Radar dataset composition (sequences, weather, traffic conditions) is not described, and the choice of 5100/2400/2466 train/test/validation frames is not justified. If the selection favors easy scenes or particular sensor configurations, the reported gains may not generalize. The authors need to specify the selection protocol, ideally with a random or stratified split and a comparison with the full dataset statistics.
  3. [Sec. II and Sec. IV-C, Table I] RadarDistill [16] is cited as a LiDAR-to-radar distillation method, which is the closest prior art to the paper's approach of using LiDAR supervision to improve radar-only detection. It is not included in any quantitative comparison. Since the paper claims 'superior performance compared to existing mainstream approaches,' a direct comparison with RadarDistill (preferably with the same detector and data split) is necessary to isolate the benefit of CORENet's specific mask-based supervision versus generic feature distillation.
  4. [Sec. IV-B] The experimental setup says the model is trained for 80 epochs and 'we presented the best results obtained.' No standard deviation, multiple seeds, or repeated runs are reported. Given the unusually large reported gains and the AP inconsistency between tables, the reader cannot assess the stability of the results. The authors should report mean and standard deviation over at least three runs, or otherwise justify why a single run is sufficient.
  5. [Sec. III-B, Eq. (4)-(5)] The binary supervision mask labels any radar point with no LiDAR point within tau = 0.5 m as invalid. This equates 'no LiDAR neighbor within tau' with 'noise,' which may be incorrect for radar points that originate from genuine objects that LiDAR does not sample at that distance, or from multi-path reflections that carry structural information. No analysis is provided of how many points are suppressed by this mask or whether ground-truth object points are systematically retained. The validation-based choice of tau mitigates the issue to some extent, but the core premise deserves empirical scrutiny, e.g., showing precision/recall of the mask against manually or automatically labeled valid radar points.
minor comments (5)
  1. [Sec. III-A, Eq. (3), Sec. III-C] Equation (3) defines the classification loss as binary cross-entropy (BCE), while Section III-C states the classification loss uses focal loss. Please align the notation and the description.
  2. [Sec. III-B, Eq. (4)] The text says 'nearest-neighbor search with a fixed distance' but Eq. (4) defines a radius search over all points within a threshold. Clarify whether the mask uses the nearest neighbor only (as the text suggests) or all points within tau (as the equation indicates).
  3. [Sec. IV-D, Tables II-IV] Tables II-IV use checkmark symbols to denote whether a component is used. These may not render in all formats. Consider using 'Yes'/'No' or explicit model names.
  4. [Sec. I] The introduction states the method 'improves the mean accuracy of the original model by 7%-18% in 3D and BEV views.' This is vague; specify whether this refers to mean AP over categories, the Car-only AP, and the specific integration models.
  5. [Sec. III-A] The sentence 'Voxel convolution can capture local spatial information but fails to preserve topological relationships between points' is not elaborated. Define what 'topological relationships' means in this context and how HPNet captures them.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the LiDAR-derived mask is an external training target and detection AP is computed independently at inference.

full rationale

The paper's derivation chain is self-contained and non-circular. LiDAR points are turned into a binary per-voxel target by KDTree nearest-neighbor thresholding (Eqs. 4-5); HMSD-Net predicts a mask; Smooth L1 loss compares them (Eq. 6); the total loss (Eq. 7) combines detection and mask terms. The mask is not a function of the detector's own output, and the reported KITTI AP numbers are independent of the mask at inference, so the improvements are not forced by construction. Hyperparameters (tau, loss weights, MLP depth) are selected by ablations, which is standard supervised tuning rather than fitting the evaluation metric. There are no load-bearing self-citations, no imported uniqueness theorem, and no ansatz smuggled in by citation; cited prior work is used for motivation or standard components. The manuscript does contain serious correctness concerns that are not circularity: the same CasA-V+CORENet configuration appears as 49.46% 3D AP in Table I but 67.34% in Tables II-IV, and the 'effective selection' of 9966 frames (Sec. IV-A) is unexplained. These undermine trust in the claimed magnitudes, but they do not make the derivation equivalent to its inputs. The conclusion's stated limitation (LiDAR required at training) is honest and does not affect the circularity verdict.

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

The central contribution is an empirical training scheme; it relies on the assumption that LiDAR proximity is a valid label for radar point validity and on standard supervised hyperparameter tuning. No new physical entities are introduced.

free parameters (3)
  • KDTree distance threshold tau = 0.5 m
    Used in Eq. (4) to define the LiDAR-valid mask; ablated in Table III, with 0.5 m chosen as best on validation.
  • Loss weights (alpha, beta, gamma) = 1, 2, 50
    Set in Sec. IV-B for the total loss in Eq. (7); used to balance classification, regression, and mask losses.
  • Noise Predictor MLP depth = 1 layer
    Ablated in Table IV; one MLP layer selected for best validation mean AP.
assumptions (3)
  • domain assumption LiDAR point clouds are an accurate spatial reference for identifying valid radar points
    Used in Sec. III-B to generate the supervised mask; assumes LiDAR captures all relevant object surfaces within 0.5 m.
  • domain assumption Radar noise is dominated by sidelobe energy around strong targets
    Stated in Sec. I, citing [12], [13]; motivates why a spatial proximity mask can separate signal from noise.
  • domain assumption The KDTree nearest-neighbor threshold yields a reliable binary noise label
    Eq. (4)-(5); an algorithmic choice, not proven, that radar points farther than 0.5 m from any LiDAR point are noise.

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Cite this review

Pith. "Pith review of CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving." pith.science (2026). https://pith.science/paper/ZZNX7YTS

@misc{pith2026250813485,
  author       = {Pith},
  title        = {Pith review of: CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZZNX7YTS}},
  note         = {Machine review of arXiv:2508.13485}
}
read the original abstract

4D radar-based object detection has garnered great attention for its robustness in adverse weather conditions and capacity to deliver rich spatial information across diverse driving scenarios. Nevertheless, the sparse and noisy nature of 4D radar point clouds poses substantial challenges for effective perception. To address the limitation, we present CORENet, a novel cross-modal denoising framework that leverages LiDAR supervision to identify noise patterns and extract discriminative features from raw 4D radar data. Designed as a plug-and-play architecture, our solution enables seamless integration into voxel-based detection frameworks without modifying existing pipelines. Notably, the proposed method only utilizes LiDAR data for cross-modal supervision during training while maintaining full radar-only operation during inference. Extensive evaluation on the challenging Dual-Radar dataset, which is characterized by elevated noise level, demonstrates the effectiveness of our framework in enhancing detection robustness. Comprehensive experiments validate that CORENet achieves superior performance compared to existing mainstream approaches.

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Reference graph

Works this paper leans on

40 extracted references · 37 canonical work pages

  1. [16]

    G. Bang, K. Choi, J. Kim, D. Kum, J. W. Choi, 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, 2024, pp. 15491–15500

    work page 2024

  2. [1]

    Y . Cui, R. Chen, W. Chu, L. Chen, D. Tian, Y . Li, D. Cao, Deep learning for image and point cloud fusion in autonomous driving: A review, IEEE Transactions on Intelligent Transportation Systems 23 (2) (2021) 722–739

    work page 2021

  3. [2]

    B. Yang, R. Guo, M. Liang, S. Casas, R. Urtasun, Radarnet: Exploiting radar for robust perception of dynamic objects, in: Computer Vision– ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16, Springer, 2020, pp. 496–512

    work page 2020

  4. [3]

    F. J. Abdu, Y . Zhang, M. Fu, Y . Li, Z. Deng, Application of deep learning on millimeter-wave radar signals: A review, Sensors 21 (6) (2021) 1951

    work page 2021

  5. [4]

    Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving

    X. Zhang, L. Wang, J. Chen, C. Fang, L. Yang, Z. Song, G. Yang, Y . Wang, X. Zhang, J. Li, Dual radar: A multi-modal dataset with dual 4d radar for autononous driving, arXiv preprint arXiv:2310.07602

    work page Pith review arXiv

  6. [5]

    Amenta, Y

    N. Amenta, Y . J. Kil, Defining point-set surfaces, ACM Transactions on Graphics (TOG) 23 (3) (2004) 264–270

    work page 2004

  7. [6]

    Fleishman, D

    S. Fleishman, D. Cohen-Or, C. T. Silva, Robust moving least-squares fitting with sharp features, ACM transactions on graphics (TOG) 24 (3) (2005) 544–552

    work page 2005

  8. [7]

    H. Chen, M. Wei, Y . Sun, X. Xie, J. Wang, Multi-patch collaborative point cloud denoising via low-rank recovery with graph constraint, IEEE transactions on visualization and computer graphics 26 (11) (2019) 3255–3270

    work page 2019

Show all 40 references
  1. [8]

    X. Yu, L. Tang, Y . Rao, T. Huang, J. Zhou, J. Lu, Point-bert: Pre- training 3d point cloud transformers with masked point modeling, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 19313–19322

    2022

  2. [9]

    M. Du, P. Zhong, X. Cai, D. Bi, Dncnet: Deep radar signal denoising and recognition, IEEE Transactions on Aerospace and Electronic Systems 58 (4) (2022) 3549–3562

    2022

  3. [10]

    Shamsfakhr, D

    F. Shamsfakhr, D. Macii, L. Palopoli, M. Corr `a, A. Ferrari, D. Fontanelli, A multi-target detection and position tracking algorithm based on mmwave-fmcw radar data, Measurement 234 (2024) 114797

    2024

  4. [11]

    J. Xu, Z. Bi, A. Singha, T. Li, Y . Chen, Y . Zhang, mmlock: User leaving detection against data theft via high-quality mmwave radar imaging, in: 2023 32nd International Conference on Computer Com- munications and Networks (ICCCN), IEEE, 2023, pp. 1–10

    2023

  5. [12]

    S. Sun, Y . D. Zhang, 4d automotive radar sensing for autonomous vehicles: A sparsity-oriented approach, IEEE Journal of Selected Topics in Signal Processing 15 (4) (2021) 879–891

    2021

  6. [13]

    H. Liu, J. Li, Z.-C. Hao, Y . Hu, G. Xu, W. Hong, A planar millimeter- wave diffuse-reflection suppression 4d imaging radar using l-shaped switchable linearly phased array, IEEE Transactions on Radar Systems

  7. [14]

    X. Yan, J. Gao, C. Zheng, C. Zheng, R. Zhang, S. Cui, Z. Li, 2dpass: 2d priors assisted semantic segmentation on lidar point clouds, in: European Conference on Computer Vision, Springer, 2022, pp. 677– 695

    2022

  8. [15]

    Zhang, R

    J. Zhang, R. Liu, H. Shi, K. Yang, S. Reiß, K. Peng, H. Fu, K. Wang, R. Stiefelhagen, Delivering arbitrary-modal semantic segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 1136–1147

    2023

  9. [17]

    Palmer, M

    P. Palmer, M. Kr ¨uger, S. Sch¨utte, R. Altendorfer, G. Adam, T. Bertram, Lerojd: Lidar extended radar-only object detection, in: European Conference on Computer Vision, Springer, 2024, pp. 379–396

    2024

  10. [18]

    Svenningsson, F

    P. Svenningsson, F. Fioranelli, A. Yarovoy, Radar-pointgnn: Graph based object recognition for unstructured radar point-cloud data, in: 2021 IEEE Radar Conference (RadarConf21), IEEE, 2021, pp. 1–6

    2021

  11. [19]

    W. Shi, R. Rajkumar, Point-gnn: Graph neural network for 3d object detection in a point cloud, in: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, 2020, pp. 1711–1719

    2020

  12. [20]

    Ulrich, S

    M. Ulrich, S. Braun, D. K ¨ohler, D. Niederl ¨ohner, F. Faion, C. Gl ¨aser, H. Blume, Improved orientation estimation and detection with hybrid object detection networks for automotive radar, in: 2022 IEEE 25th In- ternational Conference on Intelligent Transportation Systems (...

    2022

  13. [21]

    Y . Kim, S. Kim, J. W. Choi, D. Kum, Craft: Camera-radar 3d object detection with spatio-contextual fusion transformer, in: Proceedings of the AAAI Conference on Artificial Intelligence, V ol. 37, 2023, pp. 1160–1168

    2023

  14. [22]

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

    2023

  15. [23]

    Nabati, H

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

    2021

  16. [24]

    Y . Wang, J. Deng, Y . Li, J. Hu, C. Liu, Y . Zhang, J. Ji, W. Ouyang, Y . Zhang, Bi-lrfusion: Bi-directional lidar-radar fusion for 3d dynamic object detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 13394–13403

    2023

  17. [25]

    T. Zhou, J. Chen, Y . Shi, K. Jiang, M. Yang, D. Yang, Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection, IEEE Transactions on Intelligent Vehicles 8 (2) (2023) 1523–1535

    2023

  18. [26]

    J. Kim, M. Seong, G. Bang, D. Kum, J. W. Choi, Rcm-fusion: Radar- camera multi-level fusion for 3d object detection, in: 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2024, pp. 18236–18242

    2024

  19. [27]

    A. C. ¨Oztireli, G. Guennebaud, M. Gross, Feature preserving point set surfaces based on non-linear kernel regression, in: Computer graphics forum, V ol. 28, Wiley Online Library, 2009, pp. 493–501

    2009

  20. [28]

    Alexa, J

    M. Alexa, J. Behr, D. Cohen-Or, S. Fleishman, D. Levin, C. T. Silva, Computing and rendering point set surfaces, IEEE Transactions on visualization and computer graphics 9 (1) (2003) 3–15

    2003

  21. [29]

    Y . Wang, Y . Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, J. M. Solomon, Dynamic graph cnn for learning on point clouds, ACM Transactions on Graphics (tog) 38 (5) (2019) 1–12

    2019

  22. [30]

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

    2017

  23. [31]

    C. R. Qi, L. Yi, H. Su, L. J. Guibas, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, Advances in neural information processing systems 30

  24. [32]

    Liu, H.-X

    S.-L. Liu, H.-X. Guo, H. Pan, P.-S. Wang, X. Tong, Y . Liu, Deep implicit moving least-squares functions for 3d reconstruction, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 1788–1797

    2021

  25. [33]

    de Silva Edirimuni, X

    D. de Silva Edirimuni, X. Lu, Z. Shao, G. Li, A. Robles-Kelly, Y . He, Iterativepfn: True iterative point cloud filtering, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 13530–13539

    2023

  26. [34]

    J. Deng, S. Shi, P. Li, W. Zhou, Y . Zhang, H. Li, V oxel r-cnn: Towards high performance voxel-based 3d object detection, in: Proceedings of the AAAI conference on artificial intelligence, V ol. 35, 2021, pp. 1201–1209

    2021

  27. [35]

    H. Zhou, Y . Feng, M. Fang, M. Wei, J. Qin, T. Lu, Adaptive graph convolution for point cloud analysis, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 4965–4974

    2021

  28. [36]

    Graham, M

    B. Graham, M. Engelcke, L. Van Der Maaten, 3d semantic segmenta- tion with submanifold sparse convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 9224–9232

    2018

  29. [37]

    C. Liu, S. Yu, M. Yu, B. Wei, B. Li, G. Li, W. Huang, Adaptive smooth l1 loss: A better way to regress scene texts with extreme aspect ratios, in: 2021 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2021, pp. 1–7

    2021

  30. [38]

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

    2019

  31. [39]

    Sheng, S

    H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X.-S. Hua, M.-J. Zhao, G. H. Lee, Rethinking iou-based optimization for single-stage 3d object detection, in: European Conference on Computer Vision, Springer, 2022, pp. 544–561

    2022

  32. [40]

    H. Wu, J. Deng, C. Wen, X. Li, C. Wang, J. Li, Casa: A cascade attention network for 3-d object detection from lidar point clouds, IEEE Transactions on Geoscience and Remote Sensing 60 (2022) 1– 11

    2022

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Reviewed August 5, 2026 · model on record in the stance chip above.