REVIEW 3 major objections 2 minor 59 references
Subparsec Jet Morphologies of M87 at 43 GHz: Effects of Asymmetric Plasmoids within the Jet
T0 review · 3 major / 2 minor · reviewed 2026-08-15 · deepseek-v4-flash
Pith's one-line read This paper argues that one-sided injection of magnetized plasma blobs into the force-free M87 jet can explain the time-varying limb-brightening and velocity changes seen at 43 GHz on subparsec scales.
desk verdict The abstract describes a plausible testable M87 jet model, but the supplied full text is an unrelated point-cloud paper, so the actual claims cannot be evaluated from this package. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing mechanism is the force-free jet plus asymmetric plasmoid injection: a jet whose dynamics is set by magnetic field lines attached to the central black hole, in which discrete magnetized plasma blobs are injected on one side rather than in stationary symmetric fashion. The poloidal dominance of the velocity at subparsec scales is what turns the injection asymmetry into a visible, time-dependent limb-brightening pattern; without it the injected blob trajectories would not produce the observed changing bright-limb structure.
What would settle it
Take a time sequence of 43 GHz images of M87 at subparsec scales and track the bright limb and any associated knot motions: if a brightening appears first at one edge but the corresponding velocity pattern does not follow the magnetic-field-guided trajectory predicted by the force-free model, or if the bright limb switches without any new plasmoid appearing in the model's injection region, the asymmetric-injection explanation would be refuted.
Extended reading notes
Core claim
The central claim is that the observed 43 GHz limb-brightened subparsec M87 jet, whose brighter side wanders over time, is produced by asymmetric injection of plasmoids rather than stationary symmetric mass loading. In the model, relativistic plasma dynamics is governed by the large-scale magnetic field attached to the black hole, and at subparsec scales the jet velocity is dominated by poloidal velocity. The injected blobs of shearing magnetized plasma therefore follow trajectories that determine which limb appears bright and how that appearance changes, and these modeled morphologies and kinematics match the observed M87 jet variations.
Load-bearing premise
The argument stands on the premise that the subparsec M87 jet is force-free, with plasma motion governed by the large-scale magnetic field and dominated by poloidal velocity; if pressure, shocks, or instabilities control the morphology, the injected plasmoids need not match what is observed.
Editorial extensions
If this is right
- If asymmetric injection is correct, 43 GHz limb-brightening variability can be read as a clock of mass-loading events near the jet base, not as stationary structure.
- The brighter limb side is expected to alternate or shift as successive plasmoids are injected, matching single-epoch images in which neither limb stays fixed.
- At subparsec scales, apparent jet velocities are dominated by the poloidal flow, so measured knot motions trace field-guided plasma trajectories rather than unrelated shock patterns.
- Stationary symmetric mass-loading models are insufficient by themselves to reproduce the observed time-dependent morphology; injection asymmetry is needed.
Reading between the lines
- A testable extension: if injection asymmetry drives the limb pattern, simultaneous multi-frequency observations should show the bright-limb side changing in a time-ordered sequence correlated with new plasma ejections, something a single-epoch comparison cannot confirm.
- The same model could be applied to other radio jets that show limb-brightening, predicting that their bright-limb variability scales with jet magnetic-field geometry and injection rate.
- If future polarimetric 43 GHz images resolve the magnetic field orientation, one could distinguish field-guided plasmoid motion from pressure-driven instabilities, which the force-free assumption leaves out.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript's abstract (arXiv:2508.17023, astro-ph.HE) claims that a force-free jet model with asymmetric plasmoid injections can explain the temporal limb-brightening variations, velocity variations, and morphological dynamics of the M87 subparsec jet at 43 GHz. The abstract asserts that poloidal velocity dominates at these scales and that injecting asymmetric, shearing plasma into a force-free jet modifies the limb-brightened images, with the model then compared against 43 GHz M87 observations. However, the full text supplied for review is 'DualReg: Dual-Space Filtering and Reinforcement for Rigid Registration' (arXiv:2508.17034v2), a point-cloud registration paper by different authors. The submission therefore contains no equations, no model description, no image-generation pipeline, and no astrophysical comparison.
Significance. If the claims were substantiated, the paper could contribute to understanding the origin of the M87 jet's limb-brightening variability and apparent velocity patterns at subparsec scales, distinguishing one-sided shearing plasma injection from symmetric mass loading. This would be of interest to the VLBI and jet-physics community. However, none of the supporting material is present in the submitted manuscript: there are no machine-checked proofs, no reproducible code, no parameter-free derivations, and no quantitative comparison with observations. The significance cannot be assessed from the abstract alone, and the central claim is unsupported in the material provided.
major comments (3)
- [Full text (entire supplied document)] The submitted full text is a different paper, 'DualReg: Dual-Space Filtering and Reinforcement for Rigid Registration' (arXiv:2508.17034v2), with a different title, different authors, and no astrophysical content. None of the load-bearing components of the claimed M87 study are present: the force-free jet equations, the plasmoid injection prescription, the synthetic 43 GHz image generation, the quantitative comparison with M87 observations, or a test against alternative mechanisms. The central claim in the abstract's final sentence is therefore unsupported in the submitted manuscript.
- [Abstract, second sentence] The force-free jet model is announced but no governing equations or justification are given for treating the subparsec M87 jet as force-free with dynamics governed solely by the large-scale magnetic field attached to the black hole. Without the model equations and the parameter regime, the premise that poloidal velocity dominates the bulk motion cannot be evaluated, and the subsequent morphological statements have no demonstrated basis.
- [Abstract, final sentence] The claimed 'satisfactory explanation' is not backed by any quantitative comparison metric. The manuscript does not report fits to the observed 43 GHz limb-brightness asymmetries or velocity curves, nor does it define residuals, goodness-of-fit, or a comparison against symmetric mass loading, recollimation shocks, or instabilities. The phrase is therefore an assertion rather than a demonstrated result.
minor comments (2)
- [Abstract, first sentence] The phrase 'the brighter limb side not remaining consistently fixed at the subparsec scales' should be quantified with the epochs, timescales, and position angles of the observed limb-brightening flips, since the abstract's motivation depends on this variability.
- [Abstract, third sentence] The claim that poloidal velocity dominates at subparsec scales should be stated with the specific radius or time range over which it applies, as the velocity structure of M87 is known to evolve with distance from the core.
Circularity Check
No circularity can be established: the supplied full text is an unrelated rigid-registration paper, so the M87 plasmoid derivation chain is not present for analysis.
full rationale
The abstract claims that asymmetric plasmoid injection in a force-free jet 'offers a satisfactory explanation for the observed velocity variations and morphological dynamics of the M87 jet.' The derivation chain needed to evaluate that claim would be: (1) the force-free jet equations, (2) the plasmoid injection prescription, (3) synthetic 43 GHz image generation, and (4) a quantitative comparison with M87 observations. None of these steps appear in the supplied full text, which is 'DualReg: Dual-Space Filtering and Reinforcement for Rigid Registration,' a point-cloud-registration paper with different authors and no astrophysical content. Under the hard rule that circularity may only be claimed when a specific reduction can be quoted (e.g., Eq. X = Eq. Y by construction, or a fitted parameter renamed as a prediction), no circular step can be exhibited here because the relevant equations and comparisons are absent. The abstract's final sentence is an unsupported assertion in the material provided, not a demonstrated circular reduction. This is a completeness/mismatch problem affecting correctness or verifiability, not a circularity problem. Therefore the circularity score is 0, with the caveat that the submission as supplied does not contain the derivation needed to support its central claim.
Assumptions & free parameters
free parameters (1)
- Plasmoid injection properties (asymmetry, shear, location, rate)
assumptions (3)
- domain assumption Force-free jet model with relativistic plasma dynamics along large-scale magnetic fields attached to the central black hole is a valid description of the M87 subparsec jet.
- domain assumption At subparsec scales, jet velocity is predominantly influenced by poloidal velocity.
- ad hoc to paper Observed limb-brightening variability is attributable to plasmoid injection rather than to other mechanisms.
Cite this review
Pith. "Pith review of Subparsec Jet Morphologies of M87 at 43 GHz: Effects of Asymmetric Plasmoids within the Jet." pith.science (2026). https://pith.science/paper/NJWGIW2Y
@misc{pith2026250817023,
author = {Pith},
title = {Pith review of: Subparsec Jet Morphologies of M87 at 43 GHz: Effects of Asymmetric Plasmoids within the Jet},
year = {2026},
howpublished = {\url{https://pith.science/paper/NJWGIW2Y}},
note = {Machine review of arXiv:2508.17023}
}
read the original abstract
Radio observations of the M87 jet reveal limb-brightened features that exhibit temporal variations, with the brighter limb side not remaining consistently fixed at the subparsec scales. Utilizing a force-free jet model that considers exclusively the relativistic plasma dynamics along large-scale magnetic fields attached onto the central black hole, we examine the effects of asymmetric plasmoid injections within the subparsec jet. At subparsec scales, the jet velocity is predominantly influenced by poloidal velocity, leading to distinctive characteristics in both the morphology and trajectories of the plasmoids injected within the jet. We explore the potential modifications to the limb-brightened subparsec jet images resulting from the injection of asymmetric, shearing plasma, which is traditionally considered solely under conditions of stationary and symmetric mass loading. By comparing the model jet properties with the 43 GHz observations of the M87 jet, it is suggested that the asymmetric injection of plasmoids within the jet offers a satisfactory explanation for the observed velocity variations and morphological dynamics of the M87 jet.
Reference graph
Works this paper leans on
-
[1]
D3Feat: Joint Learning of Dense Detec- tion and Description of 3D Local Features
Xuyang Bai, Zixin Luo, Lei Zhou, Hongbo Fu, Long Quan, and Chiew-Lan Tai. D3Feat: Joint Learning of Dense Detec- tion and Description of 3D Local Features. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6359–6367, 2020. 2
work page 2020
-
[2]
Pointdsc: Ro- bust point cloud registration using deep spatial consistency
Xuyang Bai, Zixin Luo, Lei Zhou, Hongkai Chen, Lei Li, Zeyu Hu, Hongbo Fu, and Chiew-Lan Tai. Pointdsc: Ro- bust point cloud registration using deep spatial consistency. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15859–15869, 2021. 1, 3, 6, 7
work page 2021
-
[3]
Daniel Barath and Ji ˇr´ı Matas. Graph-cut ransac. InProceed- ings of the IEEE conference on computer vision and pattern recognition, pages 6733–6741, 2018. 2, 3
work page 2018
-
[4]
Method for registration of 3-d shapes
Paul J Besl and Neil D McKay. Method for registration of 3-d shapes. InSensor fusion IV: control paradigms and data structures, pages 586–606. Spie, 1992. 1, 3, 6
work page 1992
-
[5]
Sparse iterative closest point
Sofien Bouaziz, Andrea Tagliasacchi, and Mark Pauly. Sparse iterative closest point. InComputer graphics forum, pages 113–123. Wiley Online Library, 2013. 2, 3
work page 2013
-
[6]
Yang Chen and G ´erard Medioni. Object modelling by regis- tration of multiple range images.Image and vision comput- ing, 10(3):145–155, 1992. 1
work page 1992
-
[7]
Sc2-pcr: A second order spatial compatibility for efficient and robust point cloud registration
Zhi Chen, Kun Sun, Fan Yang, and Wenbing Tao. Sc2-pcr: A second order spatial compatibility for efficient and robust point cloud registration. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13221–13231, 2022. 3, 6, 7
work page 2022
-
[8]
Fully convolutional geometric features
Christopher Choy, Jaesik Park, and Vladlen Koltun. Fully convolutional geometric features. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 8958–8966, 2019. 1, 2, 3
work page 2019
Show all 59 references
-
[9]
Deep Global Registration
Christopher Choy, Wei Dong, and Vladlen Koltun. Deep Global Registration. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 2514–2523, 2020. 3, 7, 2
2020
-
[10]
Kuo-Liang Chung and Wei-Tai Chang. Centralized ransac- based point cloud registration with fast convergence and high accuracy.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17:5431–5442, 2024. 4
2024
-
[11]
Wenxia Dai, Hongyang Kan, Renchun Tan, Bisheng Yang, Qingfeng Guan, Ningning Zhu, Wen Xiao, and Zhen Dong. Multisource forest point cloud registration with semantic- guided keypoints and robust RANSAC mechanisms.Inter- national Journal of Applied Earth Observation and Geoin- f...
2022
-
[12]
Ppfnet: Global context aware local features for robust 3d point matching
Haowen Deng, Tolga Birdal, and Slobodan Ilic. Ppfnet: Global context aware local features for robust 3d point matching. InProceedings of the IEEE conference on com- puter vision and pattern recognition, pages 195–205, 2018. 1, 2
2018
-
[13]
A robust loss for point cloud registration
Zhi Deng, Yuxin Yao, Bailin Deng, and Juyong Zhang. A robust loss for point cloud registration. InProceedings of the IEEE/CVF international conference on computer vision, pages 6138–6147, 2021. 3
2021
-
[14]
Clipper+: a fast max- imal clique algorithm for robust global registration.IEEE Robotics and Automation Letters, 9(4):3562–3569, 2024
Kaveh Fathian and Tyler Summers. Clipper+: a fast max- imal clique algorithm for robust global registration.IEEE Robotics and Automation Letters, 9(4):3562–3569, 2024. 3
2024
-
[15]
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981
Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981. 2, 3, 6, 7
1981
-
[16]
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3354–3361. IEEE,
-
[17]
The perfect match: 3d point cloud matching with smoothed densities
Zan Gojcic, Caifa Zhou, Jan D Wegner, and Andreas Wieser. The perfect match: 3d point cloud matching with smoothed densities. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5545– 5554, 2019. 1
2019
-
[18]
Robust regression us- ing iteratively reweighted least-squares.Communications in Statistics-theory and Methods, 6(9):813–827, 1977
Paul W Holland and Roy E Welsch. Robust regression us- ing iteratively reweighted least-squares.Communications in Statistics-theory and Methods, 6(9):813–827, 1977. 3
1977
-
[19]
Predator: Registration of 3d point clouds with low overlap
Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler. Predator: Registration of 3d point clouds with low overlap. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4267–4276, 2021. 6
2021
-
[20]
Predator: Registration of 3D Point Clouds With Low Overlap
Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler. Predator: Registration of 3D Point Clouds With Low Overlap. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4267–4276, 2021. 2
2021
-
[21]
Robust outlier rejection for 3d registra- tion with variational bayes
Haobo Jiang, Zheng Dang, Zhen Wei, Jin Xie, Jian Yang, and Mathieu Salzmann. Robust outlier rejection for 3d registra- tion with variational bayes. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1148–1157, 2023. 3, 6, 7
2023
-
[22]
A robust inlier identification algorithm for point cloud registra- tion viaℓ 0-minimization
Yinuo Jiang, Xiuchuan Tang, Cheng Cheng, and Ye Yuan. A robust inlier identification algorithm for point cloud registra- tion viaℓ 0-minimization. InAdvances in Neural Information Processing Systems, 2024. 3
2024
-
[23]
Learning compact geometric features
Marc Khoury, Qian-Yi Zhou, and Vladlen Koltun. Learning compact geometric features. InProceedings of the IEEE in- ternational conference on computer vision, pages 153–161,
-
[24]
A practicalo(n 2)outlier removal method for correspondence-based point cloud registration.IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 44(8): 3926–3939, 2022
Jiayuan Li. A practicalo(n 2)outlier removal method for correspondence-based point cloud registration.IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 44(8): 3926–3939, 2022. 1
2022
-
[25]
Clipper: A graph-theoretic framework for robust data association
Parker C Lusk, Kaveh Fathian, and Jonathan P How. Clipper: A graph-theoretic framework for robust data association. In 2021 IEEE International Conference on Robotics and Au- tomation (ICRA), pages 13828–13834. IEEE, 2021. 3
2021
-
[26]
RLSAC: reinforcement learn- ing enhanced sample consensus for end-to-end robust esti- mation
Chang Nie, Guangming Wang, Zhe Liu, Luca Cavalli, Marc Pollefeys, and Hesheng Wang. RLSAC: reinforcement learn- ing enhanced sample consensus for end-to-end robust esti- mation. InProceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 9891–9900, 2023. 2
2023
-
[27]
Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C
G. Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C. Nascimento, Rama Chellappa, and Pe- dro Miraldo. 3dregnet: A deep neural network for 3d point registration. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7191– 7201, 2020. 3
2020
-
[28]
Guaranteed outlier removal for point cloud registration with correspondences
´Alvaro Parra Bustos and Tat-Jun Chin. Guaranteed outlier removal for point cloud registration with correspondences. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 40(12):2868–2882, 2018. 1
2018
-
[29]
AA- ICP: iterative closest point with anderson acceleration
Artem L Pavlov, Grigory WV Ovchinnikov, Dmitry Yu Der- byshev, Dzmitry Tsetserukou, and Ivan V Oseledets. AA- ICP: iterative closest point with anderson acceleration. In 2018 IEEE International Conference on Robotics and Au- tomation (ICRA), pages 3407–3412. IEEE, 2018. 1, 3
2018
-
[30]
BANSAC: A dynamic bayesian network for adaptive sample consensus
Valter Piedade and Pedro Miraldo. BANSAC: A dynamic bayesian network for adaptive sample consensus. InPro- ceedings of the IEEE/CVF International Conference on Computer Vision, pages 3715–3724, 2023. 3, 5, 8, 1
2023
-
[31]
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 652–660,
-
[32]
Geotrans- former: Fast and robust point cloud registration with geo- metric transformer.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):9806–9821, 2023
Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Slobodan Ilic, Dewen Hu, and Kai Xu. Geotrans- former: Fast and robust point cloud registration with geo- metric transformer.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):9806–9821, 2023. 2
2023
-
[33]
Compatibility-guided sampling consensus for 3-d point cloud registration.IEEE Trans- actions on Geoscience and Remote Sensing, 58(10):7380– 7392, 2020
Siwen Quan and Jiaqi Yang. Compatibility-guided sampling consensus for 3-d point cloud registration.IEEE Trans- actions on Geoscience and Remote Sensing, 58(10):7380– 7392, 2020. 2, 3
2020
-
[34]
A symmetric objective function for icp.ACM Transactions on Graphics (TOG), 38(4):1–7,
Szymon Rusinkiewicz. A symmetric objective function for icp.ACM Transactions on Graphics (TOG), 38(4):1–7,
-
[35]
Fast point feature histograms (fpfh) for 3d registration
Radu Bogdan Rusu, Nico Blodow, and Michael Beetz. Fast point feature histograms (fpfh) for 3d registration. In2009 IEEE international conference on robotics and automation, pages 3212–3217. IEEE, 2009. 1, 2, 3
2009
-
[36]
An improved RANSAC outlier rejection method for uav-derived point.Remote Sensing, 14(19):4917, 2022
Bahram Salehi, Sina Jarahizadeh, and Amin Sarafraz. An improved RANSAC outlier rejection method for uav-derived point.Remote Sensing, 14(19):4917, 2022. 4
2022
-
[37]
Shot: Unique signatures of histograms for surface and tex- ture description.Computer Vision and Image Understand- ing, 125:251–264, 2014
Samuele Salti, Federico Tombari, and Luigi Di Stefano. Shot: Unique signatures of histograms for surface and tex- ture description.Computer Vision and Image Understand- ing, 125:251–264, 2014. 2
2014
-
[38]
RANSAC back to SOTA: A two-stage consensus filtering for real-time 3d registration
Pengcheng Shi, Shaocheng Yan, Yilin Xiao, Xinyi Liu, Yongjun Zhang, and Jiayuan Li. RANSAC back to SOTA: A two-stage consensus filtering for real-time 3d registration. IEEE Robotics and Automation Letters, 9(12):11881–11888,
-
[39]
Least- squares rigid motion using svd, 2017
Olga Sorkine-Hornung and Michael Rabinovich. Least- squares rigid motion using svd, 2017. 6, 1
2017
-
[40]
Napsac: High noise, high dimensional robust estimation-it’s in the bag
Philip Hilaire Torr, Slawomir J Nasuto, and John Mark Bishop. Napsac: High noise, high dimensional robust estimation-it’s in the bag. InBritish Machine Vision Con- ference (BMVC), page 3, 2002. 2, 3
2002
-
[41]
Anderson acceleration for fixed-point iterations.SIAM Journal on Numerical Analysis, 49(4):1715–1735, 2011
Homer F Walker and Peng Ni. Anderson acceleration for fixed-point iterations.SIAM Journal on Numerical Analysis, 49(4):1715–1735, 2011. 3
2011
-
[42]
Turboreg: Tur- boclique for robust and efficient point cloud registration
Shaocheng Yan, Pengcheng Shi, Zhenjun Zhao, Kaixin Wang, Kuang Cao, Ji Wu, and Jiayuan Li. Turboreg: Tur- boclique for robust and efficient point cloud registration. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 26371–26381, 2025. 3, 6, 7
2025
-
[43]
Hemora: Unsupervised heuristic consensus sampling for robust point cloud registration
Shaocheng Yan, Yiming Wang, Kaiyan Zhao, Pengcheng Shi, Zhenjun Zhao, Yongjun Zhang, and Jiayuan Li. Hemora: Unsupervised heuristic consensus sampling for robust point cloud registration. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, page...
2025
-
[44]
Teaser: Fast and certifiable point cloud registration.IEEE Transactions on Robotics, 37(2):314–333, 2020
Heng Yang, Jingnan Shi, and Luca Carlone. Teaser: Fast and certifiable point cloud registration.IEEE Transactions on Robotics, 37(2):314–333, 2020. 3, 6, 7
2020
-
[45]
SAC-COT: sample consensus by sampling compatibility triangles in graphs for 3-d point cloud registra- tion.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2021
Jiaqi Yang, Zhiqiang Huang, Siwen Quan, Zhaoshuai Qi, and Yanning Zhang. SAC-COT: sample consensus by sampling compatibility triangles in graphs for 3-d point cloud registra- tion.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2021. 2, 3
2021
-
[46]
Correspondence selection with loose–tight ge- ometric voting for 3-d point cloud registration.IEEE Trans- actions on Geoscience and Remote Sensing, 60:1–14, 2022
Jiaqi Yang, Jiahao Chen, Siwen Quan, Wei Wang, and Yan- ning Zhang. Correspondence selection with loose–tight ge- ometric voting for 3-d point cloud registration.IEEE Trans- actions on Geoscience and Remote Sensing, 60:1–14, 2022. 1
2022
-
[47]
Quasi-newton solver for robust non-rigid registration
Yuxin Yao, Bailin Deng, Weiwei Xu, and Juyong Zhang. Quasi-newton solver for robust non-rigid registration. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 7597–7606, 2020. 3
2020
-
[48]
Fast and robust non-rigid registration using accelerated majorization-minimization.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):9681–9698, 2023
Yuxin Yao, Bailin Deng, Weiwei Xu, and Juyong Zhang. Fast and robust non-rigid registration using accelerated majorization-minimization.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):9681–9698, 2023. 3
2023
-
[49]
SPARE: symmetrized point-to-plane distance for robust non- rigid 3d registration.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(12):11483–11501, 2025
Yuxin Yao, Bailin Deng, Junhui Hou, and Juyong Zhang. SPARE: symmetrized point-to-plane distance for robust non- rigid 3d registration.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(12):11483–11501, 2025. 6
2025
-
[50]
3dmatch: Learning local geometric descriptors from rgb-d reconstruc- tions
Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser. 3dmatch: Learning local geometric descriptors from rgb-d reconstruc- tions. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1802–1811, 2017. 1, 2, 6
2017
-
[51]
Fast and robust iterative closest point.IEEE Transactions on Pattern Analy- sis and Machine Intelligence, 44(7):3450–3466, 2021
Juyong Zhang, Yuxin Yao, and Bailin Deng. Fast and robust iterative closest point.IEEE Transactions on Pattern Analy- sis and Machine Intelligence, 44(7):3450–3466, 2021. 1, 2, 3, 7
2021
-
[52]
An improved RANSAC-ICP method for registration of SLAM and UA V- LiDAR point cloud at plot scale.Forests, 15(6):893, 2024
Shuting Zhang, Hongtao Wang, Cheng Wang, Yingchen Wang, Shaohui Wang, and Zhenqi Yang. An improved RANSAC-ICP method for registration of SLAM and UA V- LiDAR point cloud at plot scale.Forests, 15(6):893, 2024. 4
2024
-
[53]
3d registration with maximal cliques
Xiyu Zhang, Jiaqi Yang, Shikun Zhang, and Yanning Zhang. 3d registration with maximal cliques. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17745–17754, 2023. 2, 3, 6, 7
2023
-
[54]
HyperGCT: A dy- namic hyper-gnn-learned geometric constraint for 3d regis- tration
Xiyu Zhang, Jiayi Ma, Jianwei Guo, Wei Hu, Zhaoshuai Qi, Fei Hui, Jiaqi Yang, and Yanning Zhang. HyperGCT: A dy- namic hyper-gnn-learned geometric constraint for 3d regis- tration. InProceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 24750–24759, 2025. 3
2025
-
[55]
Mac++: Going further with maximal cliques for 3d registration
Xiyu Zhang, Yanning Zhang, and Jiaqi Yang. Mac++: Going further with maximal cliques for 3d registration. In2025 International Conference on 3D Vision (3DV), pages 261–
-
[56]
Fastmac: Stochastic spectral sampling of correspondence graph
Yifei Zhang, Hao Zhao, Hongyang Li, and Siheng Chen. Fastmac: Stochastic spectral sampling of correspondence graph. InProceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 17857–17867,
-
[57]
Fast global registration
Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Fast global registration. InEuropean conference on computer vision, pages 766–782. Springer, 2016. 1, 2, 3
2016
-
[58]
Open3d: A modern library for 3d data processing.arXiv preprint arXiv:1801.09847, 2018
Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3d: A modern library for 3d data processing.arXiv preprint arXiv:1801.09847, 2018. 5 DualReg: Dual-Space Filtering and Reinforcement for Rigid Registration Supplementary Material A. Technical Details Disambiguating symmetry.In...
2018 arXiv
-
[275]
2, 3, 6, 7
IEEE, 2025. 2, 3, 6, 7
2025
Reviewed August 15, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.