Pith. sign in

REVIEW 3 major objections 3 minor 1 cited by

Structure-preserving parametric finite element methods for two-phase Stokes flow based on Lagrange multiplier approaches

T0 review · 3 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

Pith's one-line read A new Lagrange-multiplier formulation for parametric finite elements makes fully discrete two-phase Stokes solvers preserve energy decay and phase volumes exactly.

desk verdict The abstract promises a valuable structure-preserving method for two-phase Stokes flow, but the submitted full text is an unrelated radar paper, so the claims are unverifiable and the submission should be desk rejected as-is. read the letter →

arxiv 2508.12326 v1 pith:CDSKKNV6 submitted 2025-08-17 math.NA cs.NA

classification math.NAcs.NA MSC 65M6076D0765M12
keywords parametricfiniteelementmethodstwo-phaseStokesflowLagrangemultipliersenergy-decayingvolumepreservationCrank-NicolsonmethodBDF2Newton
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 aims to show that two-phase Stokes flow can be approximated by parametric finite element methods that respect two physical laws exactly, even after full discretization in space and time. The authors introduce a new set of interface conditions with additional Lagrange multipliers, and claim that the resulting fully discrete schemes preserve the energy-decaying property and the volume of each phase exactly. They pair this with Crank-Nicolson and second-order backward differentiation formula time stepping, and report that the nonlinear schemes solve efficiently with Newton's method plus a decoupling technique. If true, the work removes the usual drift and energy blow-up that plague long-time simulations of interfaces. Numerical experiments are said to confirm the expected temporal accuracy alongside exact structure preservation.

What carries the argument

The load-bearing object is the new interface condition with additional Lagrange multipliers, inserted into the parametric finite element formulation of two-phase Stokes flow. The Lagrange multipliers act as discrete forces that enforce the physical constraints at the interface, and they are chosen so that the fully discrete system inherits the energy-decaying and volume-preserving structure from the continuous problem. The time discretizations (Crank-Nicolson and BDF2) then sit on top of this structure, with Newton's method and a decoupling technique solving the resulting nonlinear systems.

What would settle it

Run the fully discrete scheme on a standard two-phase test case and measure the volume of each phase and the discrete energy after every time step; any nonzero volume change or any step at which energy increases would refute the exact-preservation claim. A second check is whether Newton's iteration converges from initial data near the solution at small time step sizes; a stall would falsify the claimed efficient solvability.

Watch

Extended reading notes

Core claim

The central discovery is that structure preservation at the discrete level can be built into the formulation itself, rather than enforced by post-processing. By choosing interface conditions with additional Lagrange multipliers in a parametric finite element setting, the authors obtain fully discrete schemes for which energy decay and volume preservation are exact identities of the discrete solution, not approximate or asymptotically recovered properties. The same formulation accommodates both Crank-Nicolson and BDF2 time discretizations; both yield nonlinear systems that the authors solve with Newton's method and a decoupling technique, and extensive numerical experiments are reported to achieve the desired temporal accuracy.

Load-bearing premise

The weakest link is the assumption that the nonlinear discrete systems are solvable and that Newton's method with the decoupling technique converges efficiently; the abstract asserts this but supplies no convergence proof, condition-number analysis, or well-posedness demonstration for the Lagrange-multiplier systems.

Editorial extensions

If this is right

  • A fully discrete two-phase Stokes solver can now be run for long times without the usual drift in phase volumes or spurious energy growth.
  • The Lagrange-multiplier interface treatment can be combined with second-order time stepping without losing exact structure preservation.
  • The nonlinear systems arising from the scheme are reported to be efficiently solvable, making the method practical despite being implicit.
  • The approach gives a template for building structure preservation into parametric finite element methods for other free-boundary problems.
  • Numerical experiments indicate that the spatial and temporal accuracy of the method matches what the underlying finite element and time discretizations promise.

Reading between the lines

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

  • The full text attached to this record is a different paper on radar object detection, so the Stokes-flow derivations and the reported experiments are not present to inspect; if that text is authoritative, the numerical claims rest on material not shown here.
  • One likely payoff not developed in the abstract is the use of these schemes in long-time simulations of drops and bubbles, where volume drift and energy blow-up are the standard failure modes; the exact preservation claim would be especially valuable there.
  • The same Lagrange-multiplier interface construction may transfer to two-phase Navier-Stokes or flows with surface tension, though the paper states results only for Stokes flow.
  • A stress point to test: if Newton's method stalls for small time steps or near topological changes, the decoupling technique rather than the structure preservation would be the limiting factor.
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

3 major / 3 minor

Summary. The abstract of arXiv:2508.12326 announces a parametric finite element method for two-phase Stokes flow in which Lagrange multipliers are used at the interface to enforce physical structure at the fully discrete level, specifically exact energy decay and volume preservation, with Crank–Nicolson and BDF2 time stepping and a Newton method with a decoupling technique. The supplied full text, however, is a different manuscript, 'DoppDrive: Doppler-Driven Temporal Aggregation for Improved Radar Object Detection' (arXiv:2508.12330v1), on automotive radar point clouds. The submission therefore contains no equations, no analysis, and no numerical experiments for the Stokes FEM problem described in the abstract.

Significance. If the abstract's claims were supported, the work would be significant for parametric FEM and two-phase flow: exact fully discrete energy dissipation and volume preservation are nontrivial structural properties, and combining them with second-order time stepping and an efficient Newton-type solve would be a useful contribution. However, none of this content is present in the submitted full text. There are no equations to check, no discrete energy identities to verify, no convergence tables, and no reproducibility artifacts; the claimed contribution cannot be credited on the basis of the abstract alone.

major comments (3)
  1. [Full text (supplied DoppDrive manuscript)] The submitted full text is an entirely different paper on radar object detection, not a two-phase Stokes FEM paper. There is no Stokes bulk equation, no interface condition involving Lagrange multipliers, no discrete energy-decay or volume-preservation argument, no Crank–Nicolson or BDF2 discretization, and no Newton decoupling technique anywhere in the document. The abstract's central claims are therefore unsupported by any derivation in the submission.
  2. [Full text (supplied DoppDrive manuscript)] The abstract promises 'extensive numerical experiments' demonstrating temporal accuracy and preservation of the two physical structures, but the supplied text contains no experiments for two-phase Stokes flow and no numerical tables or convergence diagnostics relevant to the claimed FEM. The assertions of second-order accuracy and exact structure preservation cannot be verified.
  3. [Abstract] The abstract asserts that the fully discrete schemes exactly preserve energy decay and volume and that the nonlinear systems are efficiently solved by Newton with decoupling, but it provides no well-posedness result, no convergence theorem for the nonlinear solver, and no condition-number or iteration-count data. These are load-bearing assumptions for the proposed method, and none of them is established in the submitted text.
minor comments (3)
  1. [Full text (supplied DoppDrive manuscript)] The title page and references in the supplied text concern radar detection only; if this is a packaging error, the submission must be replaced with the actual manuscript before any further review.
  2. [Abstract] The abstract should state the temporal convergence order explicitly rather than saying 'higher-order,' since both mentioned schemes, Crank–Nicolson and BDF2, are second-order methods.
  3. [Abstract] The abstract should define the spaces and unknowns (e.g., velocity, pressure, interface height, Lagrange multipliers) or refer to numbered equations in the body; currently no notation is given that a reader could check.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity detectable; the submitted full text is an unrelated radar-detection paper, so the claimed FEM derivation is entirely absent and cannot be assessed for circularity.

full rationale

The supplied full text is DoppDrive (arXiv:2508.12330v1), a radar object detection paper by Haitman and Bialer, which has no overlap with the abstract's claimed parametric finite element methods for two-phase Stokes flow. There is no derivation chain to walk, no discrete energy identity, no Lagrange multiplier system, no volume-preservation argument, no Newton decoupling analysis, and no numerical experiments for two-phase flow anywhere in the body. Per the hard rules, circularity may only be claimed when a specific reduction can be quoted and exhibited, e.g. an equation that equals its input by construction or a fitted parameter renamed as a prediction. No such reduction is present here; a mismatch between abstract and full text is a completeness or integrity failure, not circularity. The abstract's claim that the Lagrange multiplier approach preserves volume and energy is plausibly a by-design property of constrained formulations, but without the actual manuscript no further determination can be made. Therefore the appropriate circularity score is 0, with no circular steps identified.

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

Only three structural assumptions appear in the abstract. No free parameters are visible because the abstract contains no equations or data fits. The Lagrange multipliers are a standard mathematical device, not an invented physical entity, so the invented entity list is empty.

assumptions (3)
  • domain assumption Two-phase Stokes flow is described by the classical Stokes equation in the bulk with standard interface conditions.
    Stated in the abstract as the foundation; the paper relies on the PDE model and its well-posedness.
  • ad hoc to paper The Lagrange multiplier formulation at the fully discrete level is well-posed and equivalent to the original problem.
    The abstract introduces additional Lagrange multipliers as new interface conditions; existence, uniqueness, and consistency are not demonstrated in the abstract.
  • ad hoc to paper Newton's method converges for the nonlinear schemes with the decoupling technique.
    Abstract claims efficient solvability but gives no convergence analysis; practical usefulness depends on this premise.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Structure-preserving parametric finite element methods for two-phase Stokes flow based on Lagrange multiplier approaches." pith.science (2026). https://pith.science/paper/CDSKKNV6

@misc{pith2026250812326,
  author       = {Pith},
  title        = {Pith review of: Structure-preserving parametric finite element methods for two-phase Stokes flow based on Lagrange multiplier approaches},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CDSKKNV6}},
  note         = {Machine review of arXiv:2508.12326}
}
read the original abstract

We present a novel formulation for parametric finite element methods to approximate two-phase Stokes flow. The new formulation is based on the classical Stokes equation in the bulk and a novel choice of interface conditions with additional Lagrange multipliers. This new Lagrange multiplier approach ensures that the numerical methods exactly preserve two physical structures of two-phase Stokes flow at the fully discrete level: (i) the energy-decaying and (ii) the volume-preserving properties. Moreover, different types of higher-order time discretization methods are employed, including the Crank--Nicolson method and the second-order backward differentiation formula approach. The resulting schemes are nonlinear and can be efficiently solved by using the Newton method with a decoupling technique. Extensive numerical experiments demonstrate that our methods achieve the desired temporal accuracy while preserving the two physical structures of the two-phase Stokes system.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Global and local helicity-preservation in the finite element discretization of magnetic relaxation

    math.NA 2026-03 conditional novelty 6.0 of 10

    Finite element magnetic relaxation that preserves only global helicity cannot maintain braided topology that all-local-helicity preservation keeps, and this global-only scheme may emulate Taylor relaxation.

Reference graph

Works this paper leans on

49 extracted references · 43 canonical work pages · cited by 1 Pith paper

  1. [1]

    Advanced radar sensor ars540

    Continental AG. Advanced radar sensor ars540. https://www.continental- automotive.com/ en/components/radars/long- range- radars/ advanced - radar - sensor - ars540 . html. Ac- cessed: Nov. 10, 2024. 2, 6, 13

  2. [2]

    Automated driving functions: Zf captures contract for 4d full-range radar with chinese oem,

    ZF Friedrichshafen AG. Automated driving functions: Zf captures contract for 4d full-range radar with chinese oem,

  3. [3]

    GPS-IMU Sensor Fusion for Reliable Autonomous Vehicle Position Estimation

    Simegnew Yihunie Alaba. Gps-imu sensor fusion for reli- able autonomous vehicle position estimation. arXiv preprint arXiv:2405.08119, 2024. 2, 3

  4. [4]

    Milli-rio: Ego-motion esti- mation with low-cost millimetre-wave radar

    Yasin Almalioglu, Mehmet Turan, Chris Xiaoxuan Lu, Niki Trigoni, and Andrew Markham. Milli-rio: Ego-motion esti- mation with low-cost millimetre-wave radar. IEEE Sensors Journal, 21(3):3314–3323, 2020. 2, 3

  5. [5]

    Radars — advanced safety

    Aptiv. Radars — advanced safety. https : / / www . aptiv.com/en/solutions/advanced- safety/ adas/radars. Accessed: Nov. 11, 2024. 2, 6, 13

  6. [6]

    Radar transformer: An object classification network based on 4d mmw imaging radar

    Jie Bai, Lianqing Zheng, Sen Li, Bin Tan, Sihan Chen, and Libo Huang. Radar transformer: An object classification network based on 4d mmw imaging radar. Sensors, 21(11): 3854, 2021. 3

  7. [7]

    Radsimreal: Bridging the gap between synthetic and real data in radar object detec- tion with simulation

    Oded Bialer and Yuval Haitman. Radsimreal: Bridging the gap between synthetic and real data in radar object detec- tion with simulation. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 15407–15416, 2024. 6

  8. [8]

    nuscenes: A multi- modal dataset for autonomous driving

    Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom. nuscenes: A multi- modal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11621–11631, 2020. 3

Show all 49 references
  1. [9]

    Fundamentals of high accuracy inertial navigation

    Averil B Chatfield. Fundamentals of high accuracy inertial navigation. American Institute of Aeronautics and Astronau- tics, 1997. 2, 3

  2. [10]

    Pixset: An opportunity for 3d computer vision to go beyond point clouds with a full- waveform lidar dataset

    Jean-Luc D ´eziel, Pierre Merriaux, Francis Tremblay, Dave Lessard, Dominique Plourde, Julien Stanguennec, Pierre Goulet, and Pierre Olivier. Pixset: An opportunity for 3d computer vision to go beyond point clouds with a full- waveform lidar dataset. In2021 ieee international ...

  3. [11]

    Probabilistic oriented object detection in automotive radar

    Xu Dong, Pengluo Wang, Pengyue Zhang, and Langechuan Liu. Probabilistic oriented object detection in automotive radar. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition Workshops, pages 102– 103, 2020. 1

  4. [12]

    Carla: An open urban driv- ing simulator

    Alexey Dosovitskiy, German Ros, Felipe Codevilla, Anto- nio Lopez, and Vladlen Koltun. Carla: An open urban driv- ing simulator. In Conference on robot learning, pages 1–16. PMLR, 2017. 6, 13

  5. [13]

    The global positioning sys- tem and inertial navigation

    Jay Farrell and Matthew Barth. The global positioning sys- tem and inertial navigation. (No Title), 1999. 2, 3

  6. [14]

    Radargnn: Transformation invariant graph neural network for radar-based perception

    Felix Fent, Philipp Bauerschmidt, and Markus Lienkamp. Radargnn: Transformation invariant graph neural network for radar-based perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 182–191, 2023. 1, 3

  7. [15]

    A survey of automotive radar and lidar signal processing and architectures

    Luigi Giuffrida, Guido Masera, and Maurizio Martina. A survey of automotive radar and lidar signal processing and architectures. Chips, 2(4):243–261, 2023. 1

  8. [16]

    Boostrad: Enhancing object detection by boosting radar reflections

    Yuval Haitman and Oded Bialer. Boostrad: Enhancing object detection by boosting radar reflections. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1638–1647, 2024. 1

  9. [17]

    An overview of sensors in autonomous vehicles.Procedia Com- puter Science, 198:736–741, 2022

    Henry Alexander Ignatious, Manzoor Khan, et al. An overview of sensors in autonomous vehicles.Procedia Com- puter Science, 198:736–741, 2022. 1

  10. [18]

    Instantaneous ego- motion estimation using doppler radar

    Dominik Kellner, Michael Barjenbruch, Jens Klappstein, J¨urgen Dickmann, and Klaus Dietmayer. Instantaneous ego- motion estimation using doppler radar. In 16th Interna- tional IEEE Conference on Intelligent Transportation Sys- tems (ITSC 2013), pages 869–874. IEEE, 2013. 2, 3

  11. [19]

    Yolo-based simultaneous target detection and classification in automotive fmcw radar sys- tems

    Woosuk Kim, Hyunwoong Cho, Jongseok Kim, Byungkwan Kim, and Seongwook Lee. Yolo-based simultaneous target detection and classification in automotive fmcw radar sys- tems. Sensors, 20(10):2897, 2020. 1

  12. [20]

    Pointpillars: Fast encoders for object detection from point clouds

    Alex H Lang, Sourabh V ora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12697–12705, 2019. 3, 6

  13. [21]

    Feature pyra- mid networks for object detection

    Tsung-Yi Lin, Piotr Doll ´ar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyra- mid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 2117–2125, 2017. 3

  14. [22]

    Spatial multi-representation fusion for 3d object detection with 4d imaging radar., 2023

    J Liu, Q Zhao, W Xiong, T Huang, QL Han, and B Zhu SMURF. Spatial multi-representation fusion for 3d object detection with 4d imaging radar., 2023. DOI: https://doi. org/10.1109/TIV, pages 1–14, 2023. 1, 2, 3, 6, 7, 8, 11, 12, 13, 14

  15. [23]

    Radar for Fully Autonomous Driving

    Matt Markel. Radar for Fully Autonomous Driving. Artech House, 2022. 2, 6

  16. [24]

    A review of sensor technologies for per- ception in automated driving

    Enrique Marti, Miguel Angel De Miguel, Fernando Garcia, and Joshue Perez. A review of sensor technologies for per- ception in automated driving. IEEE Intelligent Transporta- tion Systems Magazine, 11(4):94–108, 2019. 1

  17. [25]

    aimotive dataset: A multimodal dataset for robust au- tonomous driving with long-range perception.arXiv preprint arXiv:2211.09445, 2022

    Tam ´as Matuszka, Iv ´an Barton, ´Ad´am Butykai, P ´eter Ha- jas, D ´avid Kiss, Domonkos Kov ´acs, S ´andor Kuns ´agi- M´at´e, P ´eter Lengyel, G ´abor N ´emeth, Levente Pet ˝o, et al. aimotive dataset: A multimodal dataset for robust au- tonomous driving with long-range perce...

  18. [26]

    Automotive radar dataset for deep learning based 3d object detection

    Michael Meyer and Georg Kuschk. Automotive radar dataset for deep learning based 3d object detection. In 2019 16th european radar conference (EuRAD), pages 129–132. IEEE,

  19. [27]

    Enhanced k-radar: Optimal density reduction to im- prove detection performance and accessibility of 4d radar tensor-based object detection

    Dong-Hee Paek, Seung-Hyun Kong, and Kevin Tirta Wi- jaya. Enhanced k-radar: Optimal density reduction to im- prove detection performance and accessibility of 4d radar tensor-based object detection. In 2023 IEEE Intelligent Ve- 9 hicles Symposium (IV), pages 1–6. IEEE, 2023. 1,...

  20. [28]

    Multi-class road user detection with 3+ 1d radar in the view-of-delft dataset.IEEE Robotics and Automation Letters, 7(2):4961–4968, 2022

    Andras Palffy, Ewoud Pool, Srimannarayana Baratam, Ju- lian FP Kooij, and Dariu M Gavrila. Multi-class road user detection with 3+ 1d radar in the view-of-delft dataset.IEEE Robotics and Automation Letters, 7(2):4961–4968, 2022. 1, 2, 3

  21. [29]

    Nvradarnet: Real-time radar obstacle and free space detection for autonomous driving

    Alexander Popov, Patrik Gebhardt, Ke Chen, and Ryan Oldja. Nvradarnet: Real-time radar obstacle and free space detection for autonomous driving. In 2023 IEEE Inter- national Conference on Robotics and Automation (ICRA) , pages 6958–6964. IEEE, 2023. 1, 2, 3, 6, 8, 14

  22. [30]

    A fast probabilistic ego-motion estimation framework for radar

    Matthias Rapp, Michael Barjenbruch, Klaus Dietmayer, Markus Hahn, and J ¨urgen Dickmann. A fast probabilistic ego-motion estimation framework for radar. In 2015 Eu- ropean Conference on Mobile Robots (ECMR) , pages 1–6. IEEE, 2015. 2, 3

  23. [31]

    Raw high-definition radar for multi-task learning

    Julien Rebut, Arthur Ouaknine, Waqas Malik, and Patrick P´erez. Raw high-definition radar for multi-task learning. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 17021–17030, 2022. 1, 3

  24. [32]

    Radar cfar thresholding in clutter and multiple target situations

    Hermann Rohling. Radar cfar thresholding in clutter and multiple target situations. IEEE transactions on aerospace and electronic systems, (4):608–621, 1983. 1, 6, 14

  25. [33]

    Road scene understanding by occupancy grid learning from sparse radar clusters using semantic segmentation

    Liat Sless, Bat El Shlomo, Gilad Cohen, and Shaul Oron. Road scene understanding by occupancy grid learning from sparse radar clusters using semantic segmentation. In Pro- ceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0–0, 2019. 1

  26. [34]

    Front radar sensor

    Bosch Mobility Solutions. Front radar sensor. https: / / www . bosch - mobility . com / en / solutions / sensors/front- radar- sensor/. Accessed: Nov. 11, 2024. 2, 6

  27. [35]

    Global positioning system: theory and ap- plications, volume I

    James J Spilker Jr, Penina Axelrad, Bradford W Parkinson, and Per Enge. Global positioning system: theory and ap- plications, volume I. American Institute of Aeronautics and Astronautics, 1996. 2, 3

  28. [36]

    Mimo radar for advanced driver-assistance systems and au- tonomous driving: Advantages and challenges

    Shunqiao Sun, Athina P Petropulu, and H Vincent Poor. Mimo radar for advanced driver-assistance systems and au- tonomous driving: Advantages and challenges. IEEE Signal Processing Magazine, 37(4):98–117, 2020. 2, 6

  29. [37]

    Radar-pointgnn: Graph based object recognition for unstructured radar point-cloud data

    Peter Svenningsson, Francesco Fioranelli, and Alexander Yarovoy. Radar-pointgnn: Graph based object recognition for unstructured radar point-cloud data. In 2021 IEEE Radar Conference (RadarConf21), pages 1–6. IEEE, 2021. 3

  30. [38]

    3-d object detection for multiframe 4-d automotive millimeter-wave radar point cloud

    Bin Tan, Zhixiong Ma, Xichan Zhu, Sen Li, Lianqing Zheng, Sihan Chen, Libo Huang, and Jie Bai. 3-d object detection for multiframe 4-d automotive millimeter-wave radar point cloud. IEEE Sensors Journal , 23(11):11125–11138, 2022. 1, 2, 3

  31. [39]

    Improved orientation estimation and detection with hybrid object detection networks for automotive radar

    Michael Ulrich, Sascha Braun, Daniel K ¨ohler, Daniel Niederl¨ohner, Florian Faion, Claudius Gl ¨aser, and Holger Blume. Improved orientation estimation and detection with hybrid object detection networks for automotive radar. In 2022 IEEE 25th International Conference on Inte...

  32. [40]

    Rpfa-net: A 4d radar pillar feature attention network for 3d object detec- tion

    Baowei Xu, Xinyu Zhang, Li Wang, Xiaomei Hu, Zhiwei Li, Shuyue Pan, Jun Li, and Yongqiang Deng. Rpfa-net: A 4d radar pillar feature attention network for 3d object detec- tion. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pages 3061–3066. IE...

  33. [41]

    Pixor: Real- time 3d object detection from point clouds

    Bin Yang, Wenjie Luo, and Raquel Urtasun. Pixor: Real- time 3d object detection from point clouds. InProceedings of the IEEE conference on Computer Vision and Pattern Recog- nition, pages 7652–7660, 2018. 3

  34. [42]

    Mask4former: Mask transformer for 4d panoptic segmentation

    Kadir Yilmaz, Jonas Schult, Alexey Nekrasov, and Bastian Leibe. Mask4former: Mask transformer for 4d panoptic segmentation. In 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages 9418–9425. IEEE,

  35. [43]

    A survey of autonomous driving: Common practices and emerging technologies.IEEE access, 8:58443– 58469, 2020

    Ekim Yurtsever, Jacob Lambert, Alexander Carballo, and Kazuya Takeda. A survey of autonomous driving: Common practices and emerging technologies.IEEE access, 8:58443– 58469, 2020. 1

  36. [44]

    Raddet: Range-azimuth-doppler based radar object detection for dynamic road users

    Ao Zhang, Farzan Erlik Nowruzi, and Robert Laganiere. Raddet: Range-azimuth-doppler based radar object detection for dynamic road users. In 2021 18th Conference on Robots and Vision (CRV), pages 95–102. IEEE, 2021. 1

  37. [45]

    Object detection and 3d estimation via an fmcw radar using a fully convolutional network

    Guoqiang Zhang, Haopeng Li, and Fabian Wenger. Object detection and 3d estimation via an fmcw radar using a fully convolutional network. In ICASSP 2020-2020 IEEE Interna- tional Conference on Acoustics, Speech and Signal Process- ing (ICASSP), pages 4487–4491. IEEE, 2020. 1

  38. [46]

    Tj4dradset: A 4d radar dataset for autonomous driving

    Lianqing Zheng, Zhixiong Ma, Xichan Zhu, Bin Tan, Sen Li, Kai Long, Weiqi Sun, Sihan Chen, Lu Zhang, Mengyue Wan, et al. Tj4dradset: A 4d radar dataset for autonomous driving. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pages 493–49...

  39. [47]

    Rc- fusion: Fusing 4-d radar and camera with bird’s-eye view features for 3-d object detection

    Lianqing Zheng, Sen Li, Bin Tan, Long Yang, Sihan Chen, Libo Huang, Jie Bai, Xichan Zhu, and Zhixiong Ma. Rc- fusion: Fusing 4-d radar and camera with bird’s-eye view features for 3-d object detection. IEEE Transactions on In- strumentation and Measurement, 72:1–14, 2023. 3, 6, 8, 14

  40. [48]

    Mmw radar-based technologies in au- tonomous driving: A review

    Taohua Zhou, Mengmeng Yang, Kun Jiang, Henry Wong, and Diange Yang. Mmw radar-based technologies in au- tonomous driving: A review. Sensors, 20(24):7283, 2020. 2, 6 10

  41. [2021]

    11, 2024

    Accessed: Nov. 11, 2024. 2, 6, 13

Pith tools

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