Pith. sign in

REVIEW 2 major objections 8 minor 56 references

Multistream Network for LiDAR and Camera-based 3D Object Detection in Outdoor Scenes

T0 review · 2 major / 8 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read MuStD fuses LiDAR and camera data in three parallel streams—pillar features, height-compressed BEV features, and a UV-Polar multimodal stream—and reports state-of-the-art or highly competitive KITTI car detection at 50–67 ms inference.

desk verdict A competitive three-stream fusion detector with genuine KITTI server results, held back by an underspecified UV projection and an overstated SOTA claim. read the letter →

arxiv 2507.19304 v1 pith:3JFX4UKI submitted 2025-07-25 cs.CV cs.AI

Muhammad Ibrahim , Naveed Akhtar , Haitian Wang , Saeed Anwar , Ajmal Mian This is my paper · ORCID
classification cs.CVcs.AI
keywords 3DobjectdetectionLiDAR-camerafusionmultimodalsparseconvolutionUVmappingpolarcoordinatesKITTIbenchmarkautonomousdriving
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

MuStD is a three-stream network for outdoor 3D object detection that combines LiDAR point clouds with RGB images. The paper's central claim is that fusing three complementary feature streams—pillar-based LiDAR features, height-compressed bird's-eye-view features, and a new 3D multimodal stream that merges RGB texture with LiDAR geometry through UV mapping and polar coordinates—achieves state-of-the-art or highly competitive accuracy on the KITTI benchmark while remaining fast enough for near-real-time use. On the official KITTI test server, the authors report car 2D APs of 97.91, 97.21, and 94.04 for Easy, Moderate, and Hard, 3D APs of 91.03, 84.36, and 80.78, and a mean orientation AOS of 96.22. If these results hold, the work demonstrates that deliberately integrating RGB information through a multi-stream fusion design is a productive direction for reliable autonomous perception.

What carries the argument

The load-bearing mechanism is the UV-Polar block inside the 3D Multimodal stream. It takes hybrid points—LiDAR points plus pseudo points generated from RGB images by depth completion—applies 3D sparse convolution, then projects the features in parallel: a UV mapping $u = x/z$, $v = y/z$ that aligns them with the RGB image plane and carries texture information, and a polar transform $(r, \theta, \phi)$ that encodes radial distance, azimuth, and elevation and carries orientation and depth relationships. Each projected feature map is processed by 2D sparse convolutions and concatenated with the original 3D sparse features to form the stream output $F_{MM}$. The LiDAR-PillarNet stream contributes pillar-based 2D geometry and the LiDAR-Height Compression stream contributes compressed bird's-eye-view features, and the final fused map $F_H$ is fed to a region-proposal detection head.

What would settle it

Inspect the released code's projection step: if raw LiDAR coordinates are fed into $u = x/z$, $v = y/z$ without applying camera extrinsics and intrinsics first, then the RGB and LiDAR features cannot be pixel-aligned; rerunning the KITTI validation ablation with the correct calibration would change the measured contribution of the 3D Multimodal stream, and if it does not, the reported fusion mechanism does not match the equations.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a carefully fused multi-stream architecture, rather than a single fusion strategy, can align and exploit both modalities for 3D detection. The central result is the server-reported KITTI car performance: mean APs of 96.39 for 2D detection, 85.39 for 3D detection, 91.34 for BEV detection, and 96.22 for orientation, with the best scores among the compared methods on the Moderate and Hard splits for 2D and orientation and on Hard for 3D and BEV. The authors attribute these gains chiefly to the 3D Multimodal stream's UV-Polar block, which projects sparse 3D features onto both an image-aligned UV grid and a polar grid and then fuses them back with the original 3D features. Their validation-set ablation shows that removing this stream lowers car 3D AP from 92.95 to 80.50, a larger drop than removing either of the two LiDAR-only streams.

Load-bearing premise

The entire multimodal stream rests on the assumption that LiDAR points can be aligned with camera pixels by the simple division $u = x/z$, $v = y/z$, yet the paper never says how the LiDAR coordinates are transformed into the camera frame or what calibration is used, so a literal implementation would leave the two modalities misaligned.

Editorial extensions

If this is right

  • If the reported KITTI test results are correct, MuStD sets a new best mean 2D car AP (96.39) and orientation AOS (96.22) among the compared methods while running at 50 ms, keeping it in the near-real-time range for autonomous driving.
  • The ablation pattern implies that the multimodal stream is the largest single accuracy contributor, since removing it costs 12.45 points of mean 3D AP on the validation set, more than removing either LiDAR-only stream.
  • The multi-class validation results suggest the fusion design transfers beyond cars, with mean 3D APs of 91.29 for cars, 68.32 for pedestrians, and 80.12 for cyclists on KITTI validation.
  • Because the UV-Polar block is a modular unit repeated at multiple scales, the architecture could be paired with other detection heads or extended to other sensor configurations without redesigning the fusion mechanism.

Reading between the lines

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

  • The paper's UV equation $u = x/z$, $v = y/z$ omits camera intrinsics and extrinsics; under a literal reading with raw LiDAR coordinates the projected features would not be pixel-aligned with the RGB image, so the real implementation almost certainly applies a calibration step that the text does not describe.
  • Because about 80% of the generated pseudo points are discarded, the method may be operating far from the depth-completion frontier; testing lower retention rates could reveal a cheaper configuration with little accuracy loss.
  • The paper reports 50 ms inference for the full method in one table and 67 ms in another; until code is released, runtime comparisons should be treated as approximate.
  • The fusion recipe—UV-mapped texture plus polar-coded geometry on shared sparse features—could carry over to other perception tasks that combine a sparse depth sensor with a dense camera, such as online mapping or lane detection, though the paper does not test those settings.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 8 minor

Summary. This manuscript proposes MuStD, a three-stream LiDAR-camera 3D object detection network. The streams are LiDAR-PillarNet, LiDAR-Height Compression, and a 3D Multimodal (MM) stream that fuses RGB and LiDAR via a proposed UV-Polar block. The authors report KITTI test-server results for car detection (2D, 3D, BEV, orientation), claiming state-of-the-art or highly competitive performance with 50-67 ms inference, and present a validation-set ablation study. The central novelty is the MM stream's combination of UV and polar/spherical feature indexing within sparse convolutions.

Significance. The KITTI test-server numbers are the paper's chief strength: they are externally generated, and the hard-category 3D AP of 80.78% and 2D mean AP of 96.39% are competitive with recent specialized methods. The architectural idea of combining UV and polar/spherical indexing inside a sparse 3D stream is a plausible and interesting contribution. The method, however, does not set a new state of the art in mean 3D AP (85.39% vs. VirConv-L's 85.56%), and the manuscript does not yet make the central fusion mechanism reproducible. The paper would be a useful addition if the projection details are supplied and the ablation is corrected.

major comments (2)
  1. [III.A, UV Mapping equation] The definition of the UV mapping as (u,v) = (x/z, y/z) is incomplete: it omits the camera intrinsic matrix (focal length, principal point), the rectification matrix, and the LiDAR-to-camera extrinsic transformation, and it does not state how continuous (u,v) values are placed into the 1600×600 feature grid. On the KITTI benchmark the standard projection is (u,v,1)^T ∝ P2 * R_rect * Tr_velo_to_cam * (x,y,z,1)^T; the formula given in the paper is normalized camera coordinates, not image pixel coordinates. Taken literally, this formula would not align LiDAR points with the RGB image, which would invalidate the multimodal fusion in the 3D MM stream. Since the UV-Polar block is the central innovation and the source of the reported performance, please provide the exact projection used, including all calibration matrices and the grid-assignment rule (e.g., integer rounding or bilinear interpolation).
  2. [IV.D, Table IV] The text in Section IV.D attributes the drop to mean 3D AP of 80.50% and 2D AP of 84.69% to 'Excluding the 3D MM Network.' However, the row in Table IV that contains these numbers has only two component checkmarks, meaning that additional components (in particular the LiDAR-PillarNet stream, and possibly the LiDAR/RGB inputs) are removed in the same configuration. As printed, the table does not support a leave-one-out interpretation, and the performance drop cannot be attributed to the 3D MM stream alone. Please present the ablation as a proper leave-one-out study over the full model, with each row removing exactly one component, and align the checkmarks with the column headers so that the configuration of each row is unambiguous.
minor comments (8)
  1. [III.A, Polar Transform] The transform described as the 'Polar Transform' is actually spherical coordinates (r, θ, φ); please rename it to avoid confusion with the polar indexing used elsewhere, or justify the terminology.
  2. [III.A, Hybrid Points Generation] The generation of pseudo points via depth completion and the retention of only 20% of them is stated without specifying the completion network, the subsampling rule, or whether the retention rate is a tuned hyperparameter; please provide these details for reproducibility.
  3. [III.A, UV-Polar block equation] In the UV-Polar block equation, the symbol W is used for both the 3D sparse convolution kernel and the 2D sparse convolution kernel, even though these act on tensors of different dimension; use distinct symbols (e.g., W3D and W2D) to make the operation unambiguous.
  4. [III.D, Feature Fusion] The fusion equation concatenates 2D BEV/Pillar features with 3D sparse features from the MM stream; the manuscript does not explain how the dimensional mismatch is resolved (e.g., by compressing the 3D features to BEV or by broadcasting). Please clarify the alignment and the exact operation denoted by ⊕.
  5. [IV.A, Table III] The inference time is reported as 50 ms in Section IV.A and Table II but as 67 ms in Table III; please state which number is the measured latency, the hardware used, and whether the depth-completion pseudo-point generation is included in the timing.
  6. [IV, Dataset description] In Section IV, the sentence 'This dataset comprises 7,481 training and 7,518 test samples... since it contains cars' is unclear; please specify the exact train/validation split used for the ablation and for the test submission (e.g., training on the 3,712-sample train split or on the full 7,481 training+validation set).
  7. [Table IV] The checkmark alignment in Table IV is difficult to read; please reformat the table (e.g., with dedicated columns and an explicit 'full model' row) so that the ablation configurations are immediately interpretable.
  8. [References] References [4] and [5] refer to the same paper ('Sparse Fuse Dense'); please merge them and use a single citation throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: benchmark claims are verified by the external KITTI server, and no prediction is a renamed fit.

full rationale

The paper's central claims are empirical results produced by the official KITTI online evaluation server, which is an external benchmark; no parameter is fitted to the test data and then relabeled as a prediction. The network's components are described by explicit operations (UV mapping as (x/z, y/z), polar transform as (r, theta, phi), pillar discretization, max-height compression, and concatenation-based fusion), and the claimed novelty is the architectural combination of these operations rather than an equation that reduces to its own input. No uniqueness theorem or theoretical result from the authors' own prior work is invoked to force the design; references that may share author names appear only as comparison baselines or background in the related-work discussion, not as load-bearing justifications. The omission of camera intrinsics and extrinsics for the UV mapping is a reproducibility or correctness concern, not circularity, because it concerns an input transformation rather than a fitted parameter being presented as an independent prediction. The derivation chain is therefore self-contained against the external benchmark, and no circular step can be exhibited from the paper's own equations.

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

The central claim depends on standard deep learning training assumptions, on an external depth completion module (SFD), and on several hand-chosen hyperparameters. The retention of only 20% of pseudo points and the fixed 1600x600 grid resolution are not ablated. The UV mapping, as written, assumes an idealized pinhole projection without discussing sensor calibration.

free parameters (4)
  • Pseudo point retention rate = 20% retained (80% discarded)
    Chosen for efficiency; no ablation on this rate; affects the multimodal stream input.
  • UV and polar grid resolution = 1600x600 cells
    Fixed 2D feature resolution; no sensitivity analysis.
  • Stream channel sizes = 16, 32, 64 (MM); 16, 32, 32, 64, 64 (LiDAR-HC)
    Hand-selected architecture hyperparameters.
  • Height compression operator = max over z
    Design choice; alternatives (sum/mean) not compared.
assumptions (4)
  • domain assumption Depth completion method SFD [4] produces pseudo points reliable enough for fusion.
    The multimodal stream depends on these pseudo points; no independent validation in this paper.
  • domain assumption KITTI benchmark reflects real-world outdoor 3D detection performance.
    The paper's claims are entirely based on this benchmark.
  • domain assumption The UV projection u=x/z, v=y/z aligns LiDAR features with RGB features without explicit camera intrinsics or extrinsics.
    This is a simplified pinhole model that the paper does not justify against actual sensor calibration.
  • domain assumption Spherical polar discretization captures object orientation and distance.
    The conversion to spherical coordinates is stated, but the discretization into a 1600x600 grid is unspecified.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Multistream Network for LiDAR and Camera-based 3D Object Detection in Outdoor Scenes." pith.science (2026). https://pith.science/paper/3JFX4UKI

@misc{pith2026250719304,
  author       = {Pith},
  title        = {Pith review of: Multistream Network for LiDAR and Camera-based 3D Object Detection in Outdoor Scenes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3JFX4UKI}},
  note         = {Machine review of arXiv:2507.19304}
}
read the original abstract

Fusion of LiDAR and RGB data has the potential to enhance outdoor 3D object detection accuracy. To address real-world challenges in outdoor 3D object detection, fusion of LiDAR and RGB input has started gaining traction. However, effective integration of these modalities for precise object detection task still remains a largely open problem. To address that, we propose a MultiStream Detection (MuStD) network, that meticulously extracts task-relevant information from both data modalities. The network follows a three-stream structure. Its LiDAR-PillarNet stream extracts sparse 2D pillar features from the LiDAR input while the LiDAR-Height Compression stream computes Bird's-Eye View features. An additional 3D Multimodal stream combines RGB and LiDAR features using UV mapping and polar coordinate indexing. Eventually, the features containing comprehensive spatial, textural and geometric information are carefully fused and fed to a detection head for 3D object detection. Our extensive evaluation on the challenging KITTI Object Detection Benchmark using public testing server at https://www.cvlibs.net/datasets/kitti/eval_object_detail.php?&result=d162ec699d6992040e34314d19ab7f5c217075e0 establishes the efficacy of our method by achieving new state-of-the-art or highly competitive results in different categories while remaining among the most efficient methods. Our code will be released through MuStD GitHub repository at https://github.com/IbrahimUWA/MuStD.git

Figures

Figures reproduced from arXiv: 2507.19304 by the authors.

Figure 1
Figure 1. Overview of the proposed MuStD network for 3D object detection comprising three data processing streams. (1) The PillarNet Stream converts [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. LiDAR-Height Compression stream processes LiDAR points with [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. The LiDAR-PillarNet architecture voxelizes raw LiDAR data into [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Comparison of inference time (ms) against object detection accuracy [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Car detection results of our method on KITTI test set for Easy, [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: 3D and BEV Car detection results of our method on the KITTI test [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

56 extracted references · 50 canonical work pages

  1. [1]

    Multi-view 3d ob- ject detection network for autonomous driving,

    X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d ob- ject detection network for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1907–1915

    work page 2017

  2. [2]

    A survey on 3d object detection methods for autonomous driving applications,

    E. Arnold, O. Y . Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis, “A survey on 3d object detection methods for autonomous driving applications,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3782–3795, 2019

    work page 2019

  3. [3]

    Multi-modal fusion transformer for end-to-end autonomous driving,

    A. Prakash, K. Chitta, and A. Geiger, “Multi-modal fusion transformer for end-to-end autonomous driving,” in CVPR, 2021, pp. 7077–7087

    work page 2021

  4. [4]

    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 CVPR, 2022

    work page 2022

  5. [5]

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

    B. Wu, S. He, Z. Yan, W. Zeng, and L. Zhang, “Sparse fuse dense: Towards high quality 3d detection with depth completion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 5412–5421

    work page 2022

  6. [6]

    Virtual sparse con- volution for multimodal 3d object detection,

    H. Wu, C. Wen, S. Shi, X. Li, and C. Wang, “Virtual sparse con- volution for multimodal 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 21 653–21 662

    work page 2023

  7. [7]

    Vpfnet: Virtual point based feature fusion network for 3d object detection,

    W. Wang, J. Shen, Z. Wu, T. He, J. Zhang, Z. Jiang, and G. H. Lee, “Vpfnet: Virtual point based feature fusion network for 3d object detection,” in IEEE Transactions on Multimedia , vol. 24, 2022, pp. 3487–3497

    work page 2022

  8. [8]

    Graph-voi: Graph neural network based voxel information aggregation for 3d object detection,

    Y . Yang, X. Sun, Z. Zhang, K. Jia, and W. Zeng, “Graph-voi: Graph neural network based voxel information aggregation for 3d object detection,” in Proceedings of the European Conference on Computer Vision (ECCV), 2022, pp. 678–695

    work page 2022

Show all 56 references
  1. [9]

    Vpfnet: Improving 3d object detection with virtual point based lidar and stereo data fusion,

    H. Zhu, J. Deng, Y . Zhang, J. Ji, Q. Mao, H. Li, and Y . Zhang, “Vpfnet: Improving 3d object detection with virtual point based lidar and stereo data fusion,” IEEE Transactions on Multimedia , vol. 25, 2023

    2023

  2. [10]

    Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,

    X. Bai, Z. Hu, X. Zhu, Q. Huang, Y . Chen, H. Fu, and C.-L. Tai, “Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 1090– 1099

    2022

  3. [11]

    M3detr: Multi-representation, multi-scale, mutual- relation 3d object detection with transformers,

    T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis, and D. Manocha, “M3detr: Multi-representation, multi-scale, mutual- relation 3d object detection with transformers,” arXiv preprint arXiv:2104.11896, 2021

    2021 arXiv

  4. [12]

    Efficient 3d object detection based on pseudo-lidar representation,

    H. Meng, C. Li, G. Chen, L. Chen, and A. Knoll, “Efficient 3d object detection based on pseudo-lidar representation,” IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, pp. 1953–1964, 2024

    1953

  5. [13]

    Acf-net: Asymmetric cascade fusion for 3d detection with lidar point clouds and images,

    Y . Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu, and W. Ding, “Acf-net: Asymmetric cascade fusion for 3d detection with lidar point clouds and images,” IEEE Transactions on Intelligent Vehicles, vol. 9, no. 2, 2023

    2023

  6. [14]

    Multi-view 3d reconstruction with transformer,

    D. Wang, X. Cui, X. Chen, Z. Zou, T. Shi, S. Salcudean, Z. J. Wang, and R. Ward, “Multi-view 3d reconstruction with transformer,” arXiv preprint arXiv:2103.12957, 2021

    2021 arXiv

  7. [15]

    nuscenes: A multimodal dataset for autonomous driving,

    H. Caesar, V . Bankiti, A. H. Lang, S. V ora, V . E. Liong, Q. Xu, A. Krishnan, Y . Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in CVPR, 2020, pp. 11 621–11 631

    2020

  8. [16]

    Multi-scale point-wise convolutional neural networks for 3d object segmentation from lidar point clouds in large-scale environments,

    L. Ma, Y . Li, J. Li, W. Tan, Y . Yu, and M. A. Chapman, “Multi-scale point-wise convolutional neural networks for 3d object segmentation from lidar point clouds in large-scale environments,” IEEE Transac- tions on Intelligent Transportation Systems , vol. 22, no. 2, pp. 821– ...

    2021

  9. [17]

    Encode: a deep point cloud odometry network,

    Y . Zhang, L. Wang, C. Fu, Y . Dai, and J. M. Dolan, “Encode: a deep point cloud odometry network,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 14 375– 14 381

    2021

  10. [18]

    Object-centric stereo matching for 3d object detection,

    A. D. Pon, J. Ku, C. Li, and S. L. Waslander, “Object-centric stereo matching for 3d object detection,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 8383– 8389

    2020

  11. [19]

    Lidar-cs dataset: Lidar point cloud dataset with cross-sensors for 3d object detection,

    J. Fang, D. Zhou, J. Zhao, C. Wu, C. Tang, C.-Z. Xu, and L. Zhang, “Lidar-cs dataset: Lidar point cloud dataset with cross-sensors for 3d object detection,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 14 822–14 829

    2024

  12. [20]

    Frame fusion with vehicle motion prediction for 3d object detection,

    X. Li, F. Wang, N. Wang, and C. Ma, “Frame fusion with vehicle motion prediction for 3d object detection,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 4252– 4258

    2024

  13. [21]

    Offboard 3d object detection from point cloud sequences,

    C. R. Qi, Y . Zhou, M. Najibi, P. Sun, K. V o, B. Deng, and D. Anguelov, “Offboard 3d object detection from point cloud sequences,” in Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 6134–6144

    2021

  14. [22]

    Pointpillars: Fast encoders for object detection from point clouds,

    A. H. Lang, S. V ora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in CVPR, 19, pp. 12 697–12 705

  15. [23]

    Second: Sparsely embedded convolutional detection,

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

    2018

  16. [24]

    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, 2020, pp. 10 529–10 538

    2020

  17. [25]

    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 , vol. 35, no. 2, 2021, pp. 1201–1209

    2021

  18. [26]

    Vision-aided localization and navigation for au- tonomous vehicles,

    T. Ji and L. Xie, “Vision-aided localization and navigation for au- tonomous vehicles,” in 2022 IEEE 17th International Conference on Control & Automation (ICCA) . IEEE, 2022, pp. 599–604

    2022

  19. [27]

    Joint 3d proposal generation and object detection from view aggregation,

    J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. Waslander, “Joint 3d proposal generation and object detection from view aggregation,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 1–8

    2018

  20. [28]

    3d- cvf: Generating joint camera and lidar features for robust 3d object detection,

    J. Yoo, J. Kim, S. Lee, M. Roh, K. M. Choi, and T.-K. Choi, “3d- cvf: Generating joint camera and lidar features for robust 3d object detection,” in Proceedings of the European Conference on Computer Vision (ECCV), 2020, pp. 282–300

    2020

  21. [29]

    Virtual convolution for lidar-based 3d object detection,

    H. Wang, J. Li, K. Zhang, and Y .-X. Wang, “Virtual convolution for lidar-based 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 13 511–13 520

    2023

  22. [30]

    Multisensor fusion for vehicle- to-vehicle cooperative localization with object detection and point cloud matching,

    L. Gao, H. Xiang, X. Xia, and J. Ma, “Multisensor fusion for vehicle- to-vehicle cooperative localization with object detection and point cloud matching,” IEEE Sensors Journal , vol. 24, no. 7, pp. 10 865– 10 877, 2024

    2024

  23. [31]

    A survey on deep-learning-based lidar 3d object detection for autonomous driving,

    S. Y . Alaba and J. E. Ball, “A survey on deep-learning-based lidar 3d object detection for autonomous driving,” Sensors, vol. 22, no. 24,

  24. [32]

    Nrconv: Noise- resistant convolution for point cloud processing,

    H. Wang, B. Li, X. Song, H. Li, and M. Liu, “Nrconv: Noise- resistant convolution for point cloud processing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 12 011–12 020

    2023

  25. [33]

    2dpass: 2d priors assisted semantic segmentation of 3d scenes,

    Y . Ye, Y . Wang, X. Yang, S. Wang, Z. Huang, B. Feng, and K. Jia, “2dpass: 2d priors assisted semantic segmentation of 3d scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1609–1618

    2021

  26. [34]

    Ted: Transformation-equivariant 3d detector,

    H. Zhou, X. Wang, L. Chen, X. Luo, H. Zhang, and K. Wu, “Ted: Transformation-equivariant 3d detector,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , 2023, pp. 2434–2442

    2023

  27. [35]

    Semantic segmentation of 3d lidar data using deep learning: a review of projection-based methods,

    A. Jhaldiyal and N. Chaudhary, “Semantic segmentation of 3d lidar data using deep learning: a review of projection-based methods,” Applied Intelligence, vol. 53, pp. 6844–6855, 2023

    2023

  28. [36]

    Tinypillarnet: Tiny pillar-based network for 3d point cloud object detection at edge,

    Y . Li, Y . Zhang, and R. Lai, “Tinypillarnet: Tiny pillar-based network for 3d point cloud object detection at edge,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 3, 2023

    2023

  29. [37]

    Multi-view reprojection architec- ture for orientation estimation,

    H. M. Choi, H. Kang, and Y . Hyun, “Multi-view reprojection architec- ture for orientation estimation,” in The IEEE International Conference on Computer Vision (ICCV) Workshops , Oct 2019

    2019

  30. [38]

    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) . IEEE, 2020

    2020

  31. [39]

    Spanet: Spatial and part-aware aggregation network for 3d object detection,

    Y . Ye, “Spanet: Spatial and part-aware aggregation network for 3d object detection,” in Pacific Rim International Conference on Artificial Intelligence. Springer, 2021, pp. 308–320

    2021

  32. [40]

    V oxel transformer for 3d object detection,

    J. Mao, Y . Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu, and C. Xu, “V oxel transformer for 3d object detection,” in ICCV, 2021

    2021

  33. [41]

    Pyramid r-cnn: Towards better performance and adaptability for 3d object detection,

    J. Mao, M. Niu, H. Bai, X. Liang, H. Xu, and C. Xu, “Pyramid r-cnn: Towards better performance and adaptability for 3d object detection,” in ICCV, 2021

    2021

  34. [42]

    3d dual-fusion: Dual-domain dual-query camera-lidar fusion for 3d object detection,

    Y . Kim, K. Park, M. Kim, D. Kum, and J. W. Choi, “3d dual-fusion: Dual-domain dual-query camera-lidar fusion for 3d object detection,” arXiv preprint arXiv:2211.13529 , 2022

    2022 arXiv

  35. [43]

    Casa: A cascade attention network for 3d object detection from lidar point clouds,

    H. Wu, J. Deng, C. Wen, X. Li, and C. Wang, “Casa: A cascade attention network for 3d object detection from lidar point clouds,” IEEE Transactions on Geoscience and Remote Sensing , 2022

    2022

  36. [44]

    Graph r-cnn: Towards accurate 3d object detection with semantic-decorated local graph,

    H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He, and D. Cai, “Graph r-cnn: Towards accurate 3d object detection with semantic-decorated local graph,” in ECCV, 2022

    2022

  37. [45]

    Dense voxel fusion for 3d object detection,

    A. Mahmoud, J. S. Hu, and S. L. Waslander, “Dense voxel fusion for 3d object detection,” WACV, 2023

    2023

  38. [46]

    Octr: Octree-based transformer for 3d object detection,

    C. Zhou, Y . Zhang, J. Chen, and D. Huang, “Octr: Octree-based transformer for 3d object detection,” in CVPR, 2023

    2023

  39. [47]

    Focal sparse convolu- tional networks for 3d object detection,

    Y . Chen, Y . Li, X. Zhang, J. Sun, and J. Jia, “Focal sparse convolu- tional networks for 3d object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2022

    2022

  40. [48]

    Transformation- equivariant 3d object detection for autonomous driving,

    H. Wu, C. Wen, W. Li, R. Yang, and C. Wang, “Transformation- equivariant 3d object detection for autonomous driving,” in AAAI, 2023

    2023

  41. [49]

    Mlf-det: Multi- level fusion for cross- modal 3d object detection,

    Z. Lin, Y . Shen, S. Zhou, S. Chen, and N. Zheng, “Mlf-det: Multi- level fusion for cross- modal 3d object detection,” in International Conference on Artificial Neural Networks . Springer, 2023, pp. 136– 149

    2023

  42. [50]

    Pvt-ssd: Single-stage 3d object detector with point-voxel transformer,

    H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He, and W. Ouyang, “Pvt-ssd: Single-stage 3d object detector with point-voxel transformer,” in CVPR, 2023

    2023

  43. [51]

    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 Conference on Computer Vision and Pattern Recognition (CVPR) , 2012

    2012

  44. [52]

    Improving 3d object detection with channel-wise transformer,

    H. Sheng, S. Cai, Y . Liu, B. Deng, J. Huang, X.-S. Hua, and M.-J. Zhao, “Improving 3d object detection with channel-wise transformer,” in Proceedings of the IEEE/CVF International Conference on Com- puter Vision, 2021, pp. 2743–2752

    2021

  45. [53]

    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 , 2021, pp. 14 494–14 503

    2021

  46. [54]

    Behind the curtain: Learning occluded shapes for 3d object detection,

    Q. Xu, Y . Zhong, and U. Neumann, “Behind the curtain: Learning occluded shapes for 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 3, 2022, pp. 2893– 2901

    2022

  47. [55]

    Multi-task multi-sensor fusion for 3d object detection,

    M. Liang, B. Yang, Y . Chen, R. Hu, and R. Urtasun, “Multi-task multi-sensor fusion for 3d object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 7345–7353

    2019

  48. [2022]

    Available: https://www.mdpi.com/1424-8220/22/24/ 9577

    [Online]. Available: https://www.mdpi.com/1424-8220/22/24/ 9577

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.