Pith. sign in

REVIEW 3 major objections 5 minor 73 references

DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising

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

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

Pith's one-line read This paper claims that denoising pseudo labels at both distribution and instance levels lets a LiDAR object detector transfer to a new unlabeled domain while keeping strong accuracy in both source and target domains.

desk verdict Two real ideas and a confounded headline: the nuScenes→KITTI gains come with an extra self-training stage no baseline gets, so treat the SOTA claim cautiously. read the letter →

arxiv 2412.08806 v1 pith:QIZJPDFW submitted 2024-12-11 cs.CV

Xiaohu Lu , Hayder Radha This is my paper · ORCID
classification cs.CV
keywords unsuperviseddomainadaptationLiDARobjectdetectionpseudolabeldenoisingpost-trainingsizenormalizationpointcloudgenerationray-constrainedsampling3Dclouds
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 tackles unsupervised domain adaptation for LiDAR-based 3D object detection, where a detector trained on a labeled source dataset must work on an unlabeled target dataset. The authors argue that pseudo labels produced by the pre-trained model carry two distinct kinds of noise: distribution-level bias in object sizes and instance-level mismatches between boxes and their point clouds. Their DALI framework removes both: post-training size normalization (PTSN) rescales the target point cloud until the mean predicted object size matches an estimated ground-truth size, and pseudo point cloud generation (PPCG) replaces noisy points inside each box with simulated points consistent with the box. Experiments on KITTI, Waymo, and nuScenes report higher target-domain AP3D than the compared methods on most tasks, and the framework also keeps source-domain accuracy that other pseudo-label methods lose.

What carries the argument

The load-bearing mechanism is a pair of procedures built around pseudo labels. PTSN (post-training size normalization) treats the predicted mean object size as a function of a point-cloud scale $s$ and picks the scale that makes the predicted mean length, width, and height match an estimated ground-truth mean; this is the object-level correction. PPCG (pseudo point cloud generation) simulates LiDAR scans of best-fitted 3D models, either CAD models or dense source-domain point clouds, to produce pseudo points for each pseudo box, with a ray-constrained version that follows the original scan rays and a constraint-free version that creates sparse far-range samples; this is the instance-level correction. The detector is then trained jointly on labeled source data and the denoised pseudo target samples, and the whole loop can be repeated.

What would settle it

Take a source-trained detector, scale target point clouds over a wide range of $s$, and plot the mean predicted box volume against $s$; if the curve is not monotone or the scale that matches the estimated target mean still leaves a predicted mean far from the ground-truth mean measured on a labeled target subset, then PTSN's core premise fails. Likewise, if ray-constrained pseudo points swapped one-for-one into the training set do not improve over the raw target points, the instance-level consistency claim would be contradicted.

Watch

Extended reading notes

Core claim

The central claim is that domain-adaptive LiDAR detection fails not because pseudo labels are noisy in general but because the noise has two separable causes, and each has a simple fix. At the distribution level, a source-trained detector predicts object sizes biased toward the source domain; PTSN shows that scaling the target point cloud by a single factor $s$ makes the mean predicted size shrink approximately as $rac{1}{s}$, so the optimal scale can be chosen by matching the predicted mean size to an SN- or ROS-based estimate of the target mean. At the instance level, even a correctly sized box can contain points that do not correspond to the object's real surface; PPCG uses a library of 3D models and a library of LiDAR sensor configurations to synthesize point clouds that are geometrically consistent with each pseudo box, either constrained to the original scan rays or generated freely to simulate hard cases. The paper claims that this two-level denoising yields better target-domain AP3D than ST3D, ST3D++, and DTS on most tested tasks, while preserving source-domain performance, and that the procedure can be iterated.

Load-bearing premise

The whole approach rests on the assumption that a pre-trained detector's mean predicted object size responds to point-cloud scaling as approximately one over the scale, so a single scale factor can align predicted and true mean object sizes; it also assumes the SN or ROS estimate of the target's true mean size is close enough to the truth.

Editorial extensions

If this is right

  • PTSN alone raises target AP3D over source-only and SN baselines on Waymo to KITTI and nuScenes to KITTI, correcting the systematic size bias in pseudo boxes.
  • Both RC-PPCG and CF-PPCG improve over PTSN alone, and combining them raises Waymo to KITTI AP3D from 63.96 to 73.52 in one iteration.
  • Because training keeps raw source samples alongside pseudo target samples, DALI maintains source-domain AP3D near the source-only level, while ST3D and ST3D++ drop sharply on the source domain.
  • PPCG can be bolted onto an existing detector as a post-processing fine-tune: freezing a pre-trained ST3D model and tuning only the head on PPCG samples raises its AP3D from 61.83 to 65.31.
  • The method transfers across backbones, since DALI(CAD) also improves on nuScenes to KITTI with PV-RCNN as the detector, and across harder tasks like Waymo to nuScenes.

Reading between the lines

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

  • If PTSN's monotone scale assumption holds for a given detector, the same procedure could turn any off-the-shelf pre-trained 3D detector into a quick domain adaptor without retraining a task-specific adaptation module.
  • The PPCG approach models object geometry explicitly, so its hardest test is non-rigid objects; the paper's own failure analysis suggests articulation or deformation models would be needed before pedestrians are handled well.
  • A testable extension is to replace the single global scale with a per-class or per-size-bin scale, which would show whether a single scale factor is sufficient or whether size bias varies by object size.
  • Because the consistency between label and points is generated rather than learned, the method may combine naturally with temporal or multi-frame aggregation, where simulated points from several viewpoints could be fused.
Share X Bluesky LinkedIn Reddit HN

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 paper proposes DALI, an unsupervised domain adaptation framework for LiDAR-based 3D object detection. It combines post-training size normalization (PTSN), which rescales target point clouds to align the predicted mean object size with an estimated target mean size, with two pseudo point cloud generation (PPCG) strategies, ray-constrained (RC-PPCG) and constraint-free (CF-PPCG), that synthesize point clouds consistent with pseudo bounding boxes. Experiments on Waymo, KITTI, and nuScenes with SECOND-IoU and PV-RCNN report state-of-the-art target-domain APs and improved source-domain preservation relative to prior pseudo-label methods, and the code is released.

Significance. If the reported results hold under a controlled comparison, DALI is a practical and interpretable addition to the 3D UDA literature. The paper's strengths include a simple, parameter-light distribution-level correction (PTSN), a mechanistically clear instance-level denoising procedure (PPCG), a released implementation, a focused ablation on Waymo->KITTI (Table VII), and a nice transferability check in which PPCG is applied to frozen ST3D backbones (Table VIII). However, the headline state-of-the-art claim is currently compromised by an uncontrolled extra self-training stage on the nuScenes->KITTI task, the task with the largest reported margins, and by the absence of any error bars or multiple-run statistics. The significance is therefore conditional on removing or properly controlling that confound.

major comments (3)
  1. [§IV-A, Table IV (nuScenes->KITTI rows)] DALI(Point) and DALI(CAD) are the only entries in Table IV marked with a double dagger indicating an additional self-training stage, which §IV-A states was added at the end of the pipeline. Because the largest margins over prior work occur on this task (e.g., +3.53 AP3D over ST3D++ in the SN-based category), the claim of outperforming leading approaches on most tasks cannot be evaluated from the current table. Please provide a controlled comparison: either apply the same self-training procedure to SN, ST3D, ST3D++, and DTS under identical conditions, or report DALI without the extra stage. The ablation in Table VII should also include a condition that removes the extra stage so the contribution of the proposed modules can be separated from generic self-training.
  2. [§IV-D, Table VI (Waymo->nuScenes row)] PTSN(w/ SN) alone yields APBEV/AP3D = 27.75/11.88, which is below Source Only (32.91/17.24) and SN (33.23/18.57) on Waymo->nuScenes. The text states that 'Theoretically, our PTSN(w/ SN) should achieve better performance than Source only in any tasks,' but this is contradicted by the paper's own results and is not a logical consequence of post-training scaling. This needs to be qualified and analyzed, for example by reporting the selected scale, the search range, and the effect of scaling on point-cloud sparsity; otherwise the distribution-level denoising claim is supported only on the two KITTI-target tasks.
  3. [§IV-D, Table VII] The PPCG ablation is conducted only on Waymo->KITTI, the task with the smallest domain gap and no extra self-training. Given the confound on nuScenes->KITTI, the paper does not demonstrate that PPCG, rather than the additional self-training stage, drives the nuScenes->KITTI gains. Please add an ablation on nuScenes->KITTI that separates PTSN, PPCG, and the self-training stage, so the reader can attribute the improvements to the proposed denoising mechanisms.
minor comments (5)
  1. [Abstract] The phrase 'novel new data' is redundant; consider 'new data' or 'novel data'.
  2. [§IV-D, paragraph after Table VI] 'the effectiveness of incorporating our PTSN and APP approaches' appears to contain a typo; 'APP' should likely be 'PPCG'.
  3. [All main tables] All reported numbers appear to come from single runs. Please state this explicitly and, ideally, report mean and standard deviation over at least three runs for the main comparisons, since several margins are small.
  4. [Fig. 3] The vertical axis label 'Vpred' is repeated; clarifying that it is the volume of Epred[Size](s) would improve readability.
  5. [§IV-A, Table II] The per-frame time of PPCG is useful, but the total training time and the time for PTSN's scale search are not reported; adding these would help practitioners assessing the method's overhead.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; PTSN calibrates to an external size estimate and headline results are external benchmark scores.

full rationale

The paper's derivation chain is empirical rather than formal. PTSN calibrates a post-training scale so that the mean predicted object size equals an external estimate (SN or ROS) of the target mean; the calibration itself is tautological in the sense that it enforces the equality, but the paper does not present this equality as a prediction of ground-truth size. The downstream claim is detection AP on real KITTI/Waymo/nuScenes benchmarks, which are external to the fitted scale. PPCG generates pseudo point clouds from CAD/point models and virtual LiDAR scans; the resulting training pairs are evaluated on real target point clouds, so the improvement is not enforced by construction. The only self-citations are [5] and [55], both in related-work context, and neither is load-bearing. The extra self-training stage applied only to nuScenes→KITTI (Sec. IV-A, Table IV) is a comparison-protocol asymmetry: the paper states 'we added the commonly used self-training [66], [73], [74] as an extra procedure at the end of the pipeline, namely, the network is further trained with the target domain and the pseudo labels to better adapt to the target domain.' This could inflate the reported margin, but it is an experimental control issue, not circular reasoning: the extra stage is a generic external procedure, not an input that is renamed as the output. No step reduces, by the paper's own equations or by self-citation, to its own inputs. Therefore the circularity score is 0.

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

The method constructs pseudo point clouds and model/sensor libraries from existing data; it introduces no new physical entities, forces, or conserved quantities. The central claim relies on four domain assumptions about detector scaling behavior, mean-size estimation accuracy, model fidelity, and the sufficiency of simulated points, all of which are plausible but only partially validated.

free parameters (4)
  • PTSN scale s = not reported per task; searched over a list of scales
    Chosen so Epred[Size](s) equals the SN/ROS estimated target mean size; a per-dataset fitted scale that directly controls pseudo box sizes.
  • Point threshold for RC-PPCG = 300 internal points
    RC-PPCG is applied only to pseudo boxes with fewer than 300 internal points; hand-chosen threshold affecting which boxes get cleaned points.
  • Number of DALI iterations = 2
    Ablation shows two iterations balance time and accuracy; this is a user-chosen stopping rule for Algorithm 1.
  • CF-PPCG far-range displacement scale = not specified
    Boxes are moved to a far location by an unspecified scale s and moved back; affects sparsity of generated CF points.
assumptions (4)
  • domain assumption Predicted mean object size scales approximately as 1/s when target point clouds are scaled by s.
    Used to justify searching a single scale for PTSN (Eq. 1, Fig. 3). Only demonstrated for one task and one backbone.
  • domain assumption SN and ROS estimates of target mean object size approximate the true target mean.
    PTSN aligns predicted sizes to these estimates (Tab. I); accuracy is checked only for Waymo to KITTI.
  • domain assumption 3D models in the library represent target objects well enough for simulated LiDAR points to be useful.
    PPCG relies on CAD or source-domain point models; no quantitative evaluation of model fitting error.
  • domain assumption Replacing raw target points with simulated pseudo points preserves the information needed for real target detection.
    Alg. 1 trains only on pseudo points for target boxes, so the simulation must be a faithful proxy.

how reviews work

0 comments
Cite this review

Pith. "Pith review of DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising." pith.science (2026). https://pith.science/paper/QIZJPDFW

@misc{pith2026241208806,
  author       = {Pith},
  title        = {Pith review of: DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QIZJPDFW}},
  note         = {Machine review of arXiv:2412.08806}
}
read the original abstract

Object detection using LiDAR point clouds relies on a large amount of human-annotated samples when training the underlying detectors' deep neural networks. However, generating 3D bounding box annotation for a large-scale dataset could be costly and time-consuming. Alternatively, unsupervised domain adaptation (UDA) enables a given object detector to operate on a novel new data, with unlabeled training dataset, by transferring the knowledge learned from training labeled \textit{source domain} data to the new unlabeled \textit{target domain}. Pseudo label strategies, which involve training the 3D object detector using target-domain predicted bounding boxes from a pre-trained model, are commonly used in UDA. However, these pseudo labels often introduce noise, impacting performance. In this paper, we introduce the Domain Adaptive LIdar (DALI) object detection framework to address noise at both distribution and instance levels. Firstly, a post-training size normalization (PTSN) strategy is developed to mitigate bias in pseudo label size distribution by identifying an unbiased scale after network training. To address instance-level noise between pseudo labels and corresponding point clouds, two pseudo point clouds generation (PPCG) strategies, ray-constrained and constraint-free, are developed to generate pseudo point clouds for each instance, ensuring the consistency between pseudo labels and pseudo points during training. We demonstrate the effectiveness of our method on the publicly available and popular datasets KITTI, Waymo, and nuScenes. We show that the proposed DALI framework achieves state-of-the-art results and outperforms leading approaches on most of the domain adaptation tasks. Our code is available at \href{https://github.com/xiaohulugo/T-RO2024-DALI}{https://github.com/xiaohulugo/T-RO2024-DALI}.

Figures

Figures reproduced from arXiv: 2412.08806 by the authors.

Figure 1
Figure 1. (a) illustrates the distributions of pseudo label volumes [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The framework of our domain adaptive LiDAR object detection method. Given the network [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Volume of Epred[Size](s) of the car class on KITTI val of different scales given the SECOND-IoU [18] network pre-trained on Waymo dataset. source domain will work better on the target domain. In SN, the deviation between the mean object sizes, namely the length, width, and height, in the source and target domains is calculated and added to the bounding box annotation of each object in the source domain. Similarly, R… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Illustration of our pseudo point clouds generation pipeline. For each target bounding box after PTSN, we first search [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Some examples of the CAD-based 3D model and point-based 3D model used in our method. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Comparison between Source only and our PTSN(w/ [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Examples of detection results of DALI, ST3D++ [19], and ST3D [18] on the task nuScenes [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Examples of ray-constrained and constraint-free pseudo point clouds and corresponding pseudo bounding boxes (in [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

73 extracted references · 59 canonical work pages

  1. [1]

    Sparse fuse dense: Towards high quality 3d detection with depth completion,

    X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu, and D. Cai, “Sparse fuse dense: Towards high quality 3d detection with depth completion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 5418–5427, 2022

    work page 2022

  2. [2]

    V oxel r- cnn: Towards high performance voxel-based 3d object detection,

    J. Deng, S. Shi, P. Li, W. Zhou, Y . Zhang, and H. Li, “V oxel r- cnn: Towards high performance voxel-based 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1201– 1209, 2021

    work page 2021

  3. [3]

    Pv-rcnn++: Point-voxel feature set abstraction with local vector representation for 3d object detection,

    S. Shi, L. Jiang, J. Deng, Z. Wang, C. Guo, J. Shi, X. Wang, and H. Li, “Pv-rcnn++: Point-voxel feature set abstraction with local vector representation for 3d object detection,”arXiv preprint arXiv:2102.00463, 2021

    arXiv 2021

  4. [4]

    Point-gnn: Graph neural network for 3d object detection in a point cloud,

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

    work page 2020

  5. [5]

    Clocs: Camera-lidar object candi- dates fusion for 3d object detection,

    S. Pang, D. Morris, and H. Radha, “Clocs: Camera-lidar object candi- dates fusion for 3d object detection,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10386–10393, IEEE, 2020

    work page 2020

  6. [6]

    A review of single-source deep unsupervised visual domain adaptation,

    S. Zhao, X. Yue, S. Zhang, B. Li, H. Zhao, B. Wu, R. Krishna, J. E. Gonzalez, A. L. Sangiovanni-Vincentelli, S. A. Seshia, et al., “A review of single-source deep unsupervised visual domain adaptation,” arXiv preprint arXiv:2009.00155, 2020

    arXiv 2009

  7. [7]

    A survey of unsupervised deep domain adaptation,

    G. Wilson and D. J. Cook, “A survey of unsupervised deep domain adaptation,” ACM Transactions on Intelligent Systems and Technology , vol. 11, no. 5, pp. 1–46, 2020

    work page 2020

  8. [8]

    Domain-adversarial training of neural networks,

    Y . Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Lavio- lette, M. Marchand, and V . Lempitsky, “Domain-adversarial training of neural networks,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 2096–2030, 2016

    2016

Show all 73 references
  1. [9]

    Adversarial discrim- inative domain adaptation,

    E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discrim- inative domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 7167–7176, 2017

    2017

  2. [10]

    Deep domain confusion: Maximizing for domain invariance,

    E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell, “Deep domain confusion: Maximizing for domain invariance,” arXiv preprint arXiv:1412.3474, 2014

    2014 arXiv

  3. [11]

    Learning transferable features with deep adaptation networks,

    M. Long, Y . Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in International Conference on Machine Learning , pp. 97–105, PMLR, 2015

    2015

  4. [12]

    Unsupervised domain adapta- tion in the wild via disentangling representation learning,

    H. Li, R. Wan, S. Wang, and A. C. Kot, “Unsupervised domain adapta- tion in the wild via disentangling representation learning,” International Journal of Computer Vision , vol. 129, pp. 267–283, 2021

    2021

  5. [13]

    Dranet: Disentangling representation and adaptation networks for unsupervised cross-domain adaptation,

    S. Lee, S. Cho, and S. Im, “Dranet: Disentangling representation and adaptation networks for unsupervised cross-domain adaptation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 15252–15261, 2021

    2021

  6. [14]

    Spherical space domain adaptation with robust pseudo-label loss,

    X. Gu, J. Sun, and Z. Xu, “Spherical space domain adaptation with robust pseudo-label loss,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 9101–9110, 2020

    2020

  7. [15]

    Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification,

    Y . Ge, D. Chen, and H. Li, “Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification,” arXiv preprint arXiv:2001.01526, 2020

    2001 arXiv

  8. [16]

    Sc-uda: Style and content gaps aware unsupervised domain adaptation for object detection,

    F. Yu, D. Wang, Y . Chen, N. Karianakis, T. Shen, P. Yu, D. Lymberopou- los, S. Lu, W. Shi, and X. Chen, “Sc-uda: Style and content gaps aware unsupervised domain adaptation for object detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vis...

    2022

  9. [17]

    Category dictionary guided unsupervised domain adaptation for object detection,

    S. Li, J. Huang, X.-S. Hua, and L. Zhang, “Category dictionary guided unsupervised domain adaptation for object detection,” in Proceedings of the AAAI conference on artificial intelligence , pp. 1949–1957, 2021

    1949

  10. [18]

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

    J. Yang, S. Shi, Z. Wang, H. Li, and X. Qi, “St3d: Self-training for un- supervised domain adaptation on 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10368–10378, 2021

    2021

  11. [19]

    St3d++: Denoised self- training for unsupervised domain adaptation on 3d object detection,

    J. Yang, S. Shi, Z. Wang, H. Li, and X. Qi, “St3d++: Denoised self- training for unsupervised domain adaptation on 3d object detection,” IEEE transactions on pattern analysis and machine intelligence, vol. 45, no. 5, pp. 6354–6371, 2022

    2022

  12. [20]

    Train in germany, test in the usa: Making 3d object detectors generalize,

    Y . Wang, X. Chen, Y . You, L. E. Li, B. Hariharan, M. Campbell, K. Q. Weinberger, and W.-L. Chao, “Train in germany, test in the usa: Making 3d object detectors generalize,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 11713– 11723, 2020

    2020

  13. [21]

    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 , pp. 3354–3361, IEEE, 2012

    2012

  14. [22]

    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

  15. [23]

    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,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 11621– 11631, 2020

    2020

  16. [24]

    Second: Sparsely embedded convolutional detection,

    Y . Yan, Y . Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors, vol. 18, no. 10, p. 3337, 2018

    2018

  17. [25]

    Dsvt: Dynamic sparse voxel transformer with rotated sets,

    H. Wang, C. Shi, S. Shi, M. Lei, S. Wang, D. He, B. Schiele, and L. Wang, “Dsvt: Dynamic sparse voxel transformer with rotated sets,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13520–13529, 2023

    2023

  18. [26]

    V oxelnet: End-to-end learning for point cloud based 3d object detection,

    Y . Zhou and O. Tuzel, “V oxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 4490–4499, 2018

    2018

  19. [27]

    Se-ssd: Self-ensembling single-stage object detector from point cloud,

    W. Zheng, W. Tang, L. Jiang, and C.-W. Fu, “Se-ssd: Self-ensembling single-stage object detector from point cloud,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 14494–14503, 2021

    2021

  20. [28]

    Pointrcnn: 3d object proposal generation and detection from point cloud,

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

    2019

  21. [29]

    3dssd: Point-based 3d single stage object detector,

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

    2020

  22. [30]

    Pv-rcnn++: Point-voxel feature set abstraction with local vector rep- resentation for 3d object detection,

    S. Shi, L. Jiang, J. Deng, Z. Wang, C. Guo, J. Shi, X. Wang, and H. Li, “Pv-rcnn++: Point-voxel feature set abstraction with local vector rep- resentation for 3d object detection,” International Journal of Computer Vision, vol. 131, no. 2, pp. 531–551, 2023

    2023

  23. [31]

    Pv- rcnn: Point-voxel feature set abstraction for 3d object detection,

    S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li, “Pv- rcnn: Point-voxel feature set abstraction for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10529–10538, 2020

    2020

  24. [32]

    Structure aware single-stage 3d object detection from point cloud,

    C. He, H. Zeng, J. Huang, X.-S. Hua, and L. Zhang, “Structure aware single-stage 3d object detection from point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 11873–11882, 2020

    2020

  25. [33]

    Moment matching for multi-source domain adaptation,

    X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang, “Moment matching for multi-source domain adaptation,” in Proceedings of the IEEE International Conference on Computer Vision , pp. 1406–1415, 2019

    2019

  26. [34]

    Progressive domain adaptation for object detection,

    H.-K. Hsu, C.-H. Yao, Y .-H. Tsai, W.-C. Hung, H.-Y . Tseng, M. Singh, and M.-H. Yang, “Progressive domain adaptation for object detection,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 749–757, 2020

    2020

  27. [35]

    Progressive feature alignment for unsupervised domain adaptation,

    C. Chen, W. Xie, W. Huang, Y . Rong, X. Ding, Y . Huang, T. Xu, and J. Huang, “Progressive feature alignment for unsupervised domain adaptation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 627–636, 2019

    2019

  28. [36]

    Reusing the task-specific classifier as a discriminator: Discriminator- free adversarial domain adaptation,

    L. Chen, H. Chen, Z. Wei, X. Jin, X. Tan, Y . Jin, and E. Chen, “Reusing the task-specific classifier as a discriminator: Discriminator- free adversarial domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 7181– 7190, 2022

    2022

  29. [37]

    Adaptive adversarial network for source- free domain adaptation,

    H. Xia, H. Zhao, and Z. Ding, “Adaptive adversarial network for source- free domain adaptation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 9010–9019, 2021

    2021

  30. [38]

    Deep subdomain alignment for cross-domain image classification,

    Y . Zhao, H. Han, S. Shan, and X. Chen, “Deep subdomain alignment for cross-domain image classification,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pp. 2820–2829, 2024

    2024

  31. [39]

    Ad- versarial robustness for unsupervised domain adaptation,

    M. Awais, F. Zhou, H. Xu, L. Hong, P. Luo, S.-H. Bae, and Z. Li, “Ad- versarial robustness for unsupervised domain adaptation,” in Proceed- ings of the IEEE/CVF International Conference on Computer Vision , pp. 8568–8577, 2021

    2021

  32. [40]

    Gradient distribution alignment certificates better adversarial domain adaptation,

    Z. Gao, S. Zhang, K. Huang, Q. Wang, and C. Zhong, “Gradient distribution alignment certificates better adversarial domain adaptation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8937–8946, 2021. 13

    2021

  33. [41]

    Rda: Robust domain adaptation via fourier adversarial attacking,

    J. Huang, D. Guan, A. Xiao, and S. Lu, “Rda: Robust domain adaptation via fourier adversarial attacking,” arXiv preprint arXiv:2106.02874 , 2021

    2021 arXiv

  34. [42]

    Conditional adversarial domain adaptation,

    M. Long, Z. Cao, J. Wang, and M. I. Jordan, “Conditional adversarial domain adaptation,” in Advances in Neural Information Processing Systems, pp. 1640–1650, 2018

    2018

  35. [43]

    Transferability vs. discrim- inability: Batch spectral penalization for adversarial domain adaptation,

    X. Chen, S. Wang, M. Long, and J. Wang, “Transferability vs. discrim- inability: Batch spectral penalization for adversarial domain adaptation,” in International Conference on Machine Learning, pp. 1081–1090, 2019

    2019

  36. [44]

    Simple: Similar pseudo label exploitation for semi-supervised classification,

    Z. Hu, Z. Yang, X. Hu, and R. Nevatia, “Simple: Similar pseudo label exploitation for semi-supervised classification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 15099–15108, 2021

    2021

  37. [45]

    Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,

    D.-H. Lee, “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in Workshop on challenges in representation learning, ICML , 2013

    2013

  38. [46]

    Adaptive consistency regularization for semi-supervised transfer learning,

    A. Abuduweili, X. Li, H. Shi, C.-Z. Xu, and D. Dou, “Adaptive consistency regularization for semi-supervised transfer learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6923–6932, 2021

    2021

  39. [47]

    Exponential moving average normalization for self-supervised and semi-supervised learning,

    Z. Cai, A. Ravichandran, S. Maji, C. Fowlkes, Z. Tu, and S. Soatto, “Exponential moving average normalization for self-supervised and semi-supervised learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 194–203, 2021

    2021

  40. [48]

    Fixbi: Bridging domain spaces for unsupervised domain adaptation,

    J. Na, H. Jung, H. J. Chang, and W. Hwang, “Fixbi: Bridging domain spaces for unsupervised domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 1094–1103, 2021

    2021

  41. [50]

    Cross domain object detection by target-perceived dual branch distillation,

    M. He, Y . Wang, J. Wu, Y . Wang, H. Li, B. Li, W. Gan, W. Wu, and Y . Qiao, “Cross domain object detection by target-perceived dual branch distillation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 9570–9580, 2022

    2022

  42. [51]

    Target-relevant knowledge preservation for multi-source domain adaptive object detection,

    J. Wu, J. Chen, M. He, Y . Wang, B. Li, B. Ma, W. Gan, W. Wu, Y . Wang, and D. Huang, “Target-relevant knowledge preservation for multi-source domain adaptive object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 5301–5310, 2022

    2022

  43. [52]

    Task-specific inconsistency alignment for domain adaptive object detection,

    L. Zhao and L. Wang, “Task-specific inconsistency alignment for domain adaptive object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 14217–14226, 2022

    2022

  44. [53]

    Multi-granularity alignment domain adaptation for object detection,

    W. Zhou, D. Du, L. Zhang, T. Luo, and Y . Wu, “Multi-granularity alignment domain adaptation for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9581–9590, 2022

    2022

  45. [54]

    Sigma: Semantic-complete graph matching for domain adaptive object detection,

    W. Li, X. Liu, and Y . Yuan, “Sigma: Semantic-complete graph matching for domain adaptive object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 5291– 5300, 2022

    2022

  46. [55]

    Integrated multiscale domain adaptive yolo,

    M. Hnewa and H. Radha, “Integrated multiscale domain adaptive yolo,” IEEE Transactions on Image Processing, vol. 32, pp. 1857–1867, 2023

    2023

  47. [56]

    Domain adaptive faster r-cnn for object detection in the wild,

    Y . Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool, “Domain adaptive faster r-cnn for object detection in the wild,” in Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 3339–3348, 2018

    2018

  48. [57]

    Faster r-cnn: Towards real-time object detection with region proposal networks,

    S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems , vol. 28, 2015

    2015

  49. [58]

    Ssda-yolo: Semi-supervised domain adaptive yolo for cross-domain object detection,

    H. Zhou, F. Jiang, and H. Lu, “Ssda-yolo: Semi-supervised domain adaptive yolo for cross-domain object detection,” Computer Vision and Image Understanding, vol. 229, p. 103649, 2023

    2023

  50. [59]

    Instance relation graph guided source-free domain adaptive object detection,

    V . Vibashan, P. Oza, and V . M. Patel, “Instance relation graph guided source-free domain adaptive object detection,” in 2023 IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR), pp. 3520– 3530, IEEE, 2023

    2023

  51. [60]

    Sf- uda 3d: Source-free unsupervised domain adaptation for lidar-based 3d object detection,

    C. Saltori, S. Lathuili ´ere, N. Sebe, E. Ricci, and F. Galasso, “Sf- uda 3d: Source-free unsupervised domain adaptation for lidar-based 3d object detection,” in 2020 International Conference on 3D Vision (3DV), pp. 771–780, IEEE, 2020

    2020

  52. [61]

    Spg: Unsu- pervised domain adaptation for 3d object detection via semantic point generation,

    Q. Xu, Y . Zhou, W. Wang, C. R. Qi, and D. Anguelov, “Spg: Unsu- pervised domain adaptation for 3d object detection via semantic point generation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 15446–15456, 2021

    2021

  53. [62]

    Unsupervised domain adaptive 3d detection with multi-level consistency,

    Z. Luo, Z. Cai, C. Zhou, G. Zhang, H. Zhao, S. Yi, S. Lu, H. Li, S. Zhang, and Z. Liu, “Unsupervised domain adaptive 3d detection with multi-level consistency,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 8866–8875, 2021

    2021

  54. [63]

    Exploiting playbacks in unsupervised domain adaptation for 3d object detection in self-driving cars,

    Y . You, C. A. Diaz-Ruiz, Y . Wang, W.-L. Chao, B. Hariharan, M. Camp- bell, and K. Q. Weinbergert, “Exploiting playbacks in unsupervised domain adaptation for 3d object detection in self-driving cars,” in 2022 International Conference on Robotics and Automation (ICRA) , pp. 5...

    2022

  55. [64]

    See eye to eye: A lidar-agnostic 3d detection framework for unsupervised multi-target domain adaptation,

    D. Tsai, J. S. Berrio, M. Shan, S. Worrall, and E. Nebot, “See eye to eye: A lidar-agnostic 3d detection framework for unsupervised multi-target domain adaptation,” IEEE Robotics and Automation Letters , 2022

    2022

  56. [65]

    Lidar distillation: Bridging the beam-induced domain gap for 3d object detection,

    Y . Wei, Z. Wei, Y . Rao, J. Li, J. Zhou, and J. Lu, “Lidar distillation: Bridging the beam-induced domain gap for 3d object detection,” arXiv preprint arXiv:2203.14956, 2022

    2022 arXiv

  57. [66]

    Cl3d: Unsupervised domain adaptation for cross-lidar 3d detection,

    X. Peng, X. Zhu, and Y . Ma, “Cl3d: Unsupervised domain adaptation for cross-lidar 3d detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2047–2055, 2023

    2023

  58. [67]

    Density-insensitive unsupervised domain adaption on 3d object detection,

    Q. Hu, D. Liu, and W. Hu, “Density-insensitive unsupervised domain adaption on 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 17556– 17566, 2023

    2023

  59. [68]

    Adaptation via proxy: Building instance-aware proxy for unsupervised domain adaptive 3d object detection,

    Z. Li, Y . Yao, Z. Quan, L. Qi, Z. Feng, and W. Yang, “Adaptation via proxy: Building instance-aware proxy for unsupervised domain adaptive 3d object detection,” IEEE Transactions on Intelligent Vehicles , 2023

    2023

  60. [69]

    Cadillac: Carnegie mellon university dataset,

    E. Toropov and J. Moura, “Cadillac: Carnegie mellon university dataset,” 2019

    2019

  61. [70]

    Openpcdet: An open-source toolbox for 3d object detection from point clouds

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

    2020

  62. [71]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

    2014 arXiv

  63. [72]

    Fast, minimum storage ray-triangle inter- section,

    T. M ¨oller and B. Trumbore, “Fast, minimum storage ray-triangle inter- section,” in ACM SIGGRAPH 2005 Courses , pp. 7–es, 2005

    2005

  64. [73]

    Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation,

    H. Yao, X. Hu, and X. Li, “Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation,” in Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3099– 3107, 2022

    2022

  65. [74]

    Reference- guided pseudo-label generation for medical semantic segmentation,

    C. M. Seibold, S. Reiß, J. Kleesiek, and R. Stiefelhagen, “Reference- guided pseudo-label generation for medical semantic segmentation,” in Proceedings of the AAAI conference on artificial intelligence , pp. 2171– 2179, 2022. Xiaohu Lu received the B.S. degree and M.S. degree ...

    2022

Pith tools

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