Pith. sign in

REVIEW 4 major objections 5 minor 46 references

RTMap: Real-Time Recursive Mapping with Change Detection and Localization

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read RTMap claims that one real-time onboard model can simultaneously build a multi-traversal HD map, localize the vehicle against it, and detect road changes, with uncertainty modeling making the fusion work.

desk verdict A useful integrated system for crowdsourced HD mapping, but the headline mapping gains are not isolated from the benefit of simply fusing more traversals. read the letter →

arxiv 2507.00980 v2 pith:V322DQXO submitted 2025-07-01 cs.CV

Yuheng Du , Sheng Yang , Lingxuan Wang , Zhenghua Hou , Chengying Cai , Zhitao Tan , Mingxia Chen , Shi-Sheng Huang
show 1 more author
Qiang Li
This is my paper · ORCID
classification cs.CV
keywords HDmappingcrowdsourcedmapschangedetectionmap-basedlocalizationuncertaintymodelingmulti-traversalfusionautonomousdrivingvectorizedmapconstruction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims to be the first end-to-end framework that treats an HD map as a growing, self-evolving memory: on every traversal the same onboard model localizes the vehicle against the prior map, flags road elements that have appeared or disappeared, and folds the current frame's detections into the prior map, all in real time. If true, fleets of vehicles could maintain fresh, accurate high-definition maps through normal driving instead of relying on offline mapping pipelines, and downstream prediction and planning would always see a map aligned to the ego pose. The central reported evidence is that after three crowdsourcing cycles RTMap reaches 57.6 mAP on straight scenarios and 55.4 mAP on turning scenarios, versus 39.9 and 28.0 for the strongest single-traversal baseline. The paper argues the gain comes from explicitly modeling per-vertex positional uncertainty and from separating matched from outdated map queries before fusing or localizing.

What carries the argument

The load-bearing object is the hybrid query set Qhybrid = {Qmap, Qfake, Qnew} + Qhie, together with existence-aware matching: during training, only genuine prior-map queries are pre-attributed to ground-truth elements, while synthetic fake queries and new queries are matched by Hungarian assignment. This training asymmetry is what lets the network tell outdated from matched elements at inference by their confidence scores. The other central mechanism is per-vertex uncertainty: each map element vertex is modeled with a univariate Laplace distribution whose scale parameter becomes a probabilistic density used both as a Mahalanobis-weighting covariance in a MAP pose optimizer and as a fusion weight in the cloud-side crowdsourcing solver. These two mechanisms together are what the paper claims convert repeated traversals into a better map and a better pose.

What would settle it

Run RTMap on a stretch of road with a known deleted lane marking, record the per-element confidence scores for the prior queries, and measure the overlap between the matched and outdated score distributions. If a matched element's confidence is often below an outdated element's, the existence-aware split cannot be doing the work the paper assigns it, and the localization and mapping gains should shrink when the split is disabled.

Watch

Extended reading notes

Core claim

RTMap's central claim is that multi-traversal online HD mapping, map-based localization, and change detection are really one problem: retrieving, matching, and differentiating map elements across traversals. The paper proposes a unified end-to-end architecture in which hybrid queries—some initialized from the prior map and some from the current sensor frame—are decoded into three classes: matched, outdated, and newly observed. Matched queries feed a pose head and a maximum-a-posteriori state estimator, outdated queries are treated as deletions, and new queries are treated as insertions; the same correspondences then drive a cloud-side probabilistic fusion that updates the crowdsourced prior map. The paper reports that this joint design outperforms single-traversal baselines on map quality after repeated cycles, improves change-detection accuracy over the previous task-specific method, and that separating outdated queries from matched ones lowers localization error.

Load-bearing premise

The load-bearing premise is that the confidence score of a prior query reliably separates map elements that still exist from elements that are outdated: the paper says 'we believe that using the confidence score of the predictions corresponding to Qprior can effectively distinguish between them.' If that separation fails, change detection degrades and outdated elements pollute both localization and the fused crowdsourced map.

Editorial extensions

If this is right

  • Multi-traversal fusion materially improves map quality: after three cycles RTMap's average mAP rises to 57.6% on straight scenes and 55.4% on turning scenes, compared with 39.9% and 28.0% for the best single-traversal baseline.
  • Separating outdated from matched prior queries improves localization: the Qmap variant beats the Qprior variant on all reported lateral, longitudinal, and yaw errors on TbV.
  • Explicit optimization-based pose estimation (TR) beats end-to-end regression (TE) on lateral and yaw errors on nuScenes, while the uncertainty-aware vertex loss further trims lateral error.
  • Uncertainty-aware fusion beats plain fusion: the version without vertex-level probabilistic density is worse at every reported cycle on TbV.

Reading between the lines

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

  • The confidence-based split between matched and outdated queries is a heuristic the paper states rather than proves; a natural test is to inspect the confidence distributions on held-out changed scenes and see whether a threshold separates them cleanly.
  • If the heuristic holds, the same hybrid-query design could be reused for other map element types beyond lanes, boundaries, and crossings—for example traffic signs or parking markings—wherever a vectorized prior exists.
  • The architecture suggests a fleet-level map updating protocol with bounded bandwidth: onboard agents upload only detected changes and uncertainty-weighted vertices, while the cloud solves for updated positions; this is a direct consequence the paper gestures at in its future-work section.
  • The evaluation compares three-cycle fused output to single-traversal baselines; readers should keep in mind that averaging more traversals would help any baseline, so the cleanest evidence for the method's specific contribution is the uncertainty ablation and the change-detection ablation rather than the headline mAP gap.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes RTMap, an end-to-end onboard framework for multi-traversal HD mapping that simultaneously performs map-based localization, change detection, and crowdsourced map fusion. The method uses hybrid queries (Qmap, Qfake, Qnew), per-vertex probabilistic density estimation, a pose auxiliary loss, and a MAP-based pose estimator and cloud-side fusion scheme. Experiments on the TbV and nuScenes datasets report improved map quality after three crowdsourcing cycles, change detection accuracy, and localization accuracy compared with several online HD mapping baselines. The paper also promises public source code release.

Significance. If the reported results are robust, RTMap addresses a practically important gap: most online HD mapping methods are single-traversal, whereas RTMap explicitly targets multi-traversal fusion, change detection, and prior-aided localization in one framework. The uncertainty-aware fusion in Eq. (5) and the use of change detection for localization outlier rejection are sensible design choices. The paper ships qualitative visualizations, ablations for the uncertainty term, and comparisons on public datasets. However, the central quantitative claim that the framework itself—rather than merely the availability of more traversals—drives the map-quality improvement is not yet supported by the experiments.

major comments (4)
  1. [Sec. 4.3, Table 1] The headline comparison is confounded by traversal count. RTMap's Cycle-3 results aggregate three traversals of the same location, while the baselines (MapTRv2, HRMapNet, MapTracker) are evaluated on a single traversal. Any method that simply aligns and averages three independent map predictions could improve mAP because it receives three times the observations. The paper does not include a multi-traversal baseline that gives the same data to an existing single-traversal method, nor does it report Cycle-1 numbers for RTMap even though Sec. 4.3 states that in the first cycle 'our approach is essentially the same as existing methods.' Without this controlled comparison, the improvement from 39.9/28.0 to 57.6/55.4 mAP cannot be attributed to RTMap's uncertainty-aware fusion or change-detection-based outlier rejection.
  2. [Sec. 3.3] The separation of Qmap from Qfake during inference rests on an unvalidated heuristic: 'we believe that using the confidence score of the predictions corresponding to Qprior can effectively distinguish between them.' This assumption is load-bearing for both change detection and the outlier rejection that improves localization, but the paper provides no quantitative evidence of separability, such as confidence distributions for matched versus outdated queries, precision/recall of the thresholding rule, or sensitivity analysis. A failure of this heuristic would degrade change detection and the localization gains reported in Table 3.
  3. [Sec. 4.2, Sec. 4.3] The crowdsourcing evaluation uses only 15 clips (6 straight, 9 turning) from TbV. No error bars, per-clip breakdown, or statistical significance tests are reported. Given the small sample size and the fact that the main quantitative claim is built on this table, the evidence is too thin to establish that the improvements generalize across scenarios. Additional clips or a per-scenario analysis would be needed to support the cross-scenario conclusions.
  4. [Eq. (5), Sec. 3.5] The description of the crowdsourcing update as a 'Gaussian mixture' is not consistent with the displayed objective. Eq. (5) is a weighted least-squares problem with a single prior term and per-observation weights; it is not a mixture model. The paper does not specify how the mixture covariance is formed or updated from the per-vertex uncertainties, nor how the union-find association interacts with the probabilistic weighting. This makes it difficult to assess whether the claimed 'probabilistic-aware' fusion is actually a MAP estimate under the stated model.
minor comments (5)
  1. [Throughout] There are several typographical issues, including 'Methology' in the Section 3 heading, 'Alex H Lang' with a stray capital in reference [3], and 'V ora' in the same reference. These should be corrected.
  2. [Sec. 3.4] The notation for the vertex loss is confusing: the sum over k=1 to 2 is written as if it is inside the logarithm term, but it should apply to the entire expression. Please clarify the exact form of Lnll.
  3. [Sec. 4.1] The phrase 'we pad the perceptual range as necessary' is vague. It would be clearer to specify the padding protocol and its effect on the training distribution of prior-map coverage.
  4. [Sec. 4.4] The change detection evaluation in Table 2 reports only aggregated accuracy. Reporting per-event precision/recall or IoU for insertion/deletion events would be more informative, especially given the claimed high recall for changed elements.
  5. [Fig. 5] The qualitative figure is dense and the zoomed regions are difficult to discern in a print version. Adding bounding boxes or arrows to highlight the specific improvements would improve readability.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: RTMap's central claims are empirical and evaluated against external data; the only circularity-adjacent issues are minor non-load-bearing co-author self-citations and an evaluation confound, neither of which reduces the result to its inputs by construction.

full rationale

RTMap makes empirical contributions rather than analytic derivations, and no step in the paper reduces a claimed prediction to its own inputs by construction. The recursive crowdsourcing loop (Eq. 5) is intentionally self-referential in operation, but each stage is validated against external ground truth (TbV, nuScenes), and the MAP estimators in Eqs. (4)-(5) are standard objective functions, not identities that force the reported accuracy. The change-detection separation of Qmap and Qfake relies on an explicit heuristic ('we believe that using the confidence score...') rather than a definitional equivalence, which is a validation risk rather than circular reasoning. The Table 1 comparison of Cycle-3 RTMap against single-traversal baselines is a genuine evaluation confound (extra traversals versus algorithmic fusion), but a confound is not the same as fitting a parameter and calling it a prediction. Several references are co-author self-citations (e.g., EgoVM [15], Yang et al. [40]), but they are used only as contextual prior art and are not load-bearing: the method is trained without crowdsourced prior maps and is benchmarked against external datasets. Overall, no definitional or fitted-input circularity was found.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on several domain assumptions and hand-chosen hyperparameters, but no new physical entities are introduced. The main free parameters are loss weights, perturbation scales, and architectural constants, all standard for a deep learning system.

free parameters (5)
  • loss weight lambda1 = 0.03
    Chosen to balance the NLL loss in Eq. 3; affects uncertainty learning and overall training.
  • loss weight lambda2 = 5.0
    Weight for the Manhattan distance loss in Eq. 3; selected by hand to balance convergence and accuracy.
  • pose translation weight = 0.04
    Scales the translation component relative to heading in the pose auxiliary loss (Sec. 3.4).
  • per-vertex point count V = 20
    Fixed number of points per map element, inherited from MapTR-style representation.
  • localization perturbation scales = lateral 0.75 m, longitudinal 1.5 m, yaw 0.85 deg
    Gaussian noise injected in evaluation to simulate realistic localization error (Sec. 4.2).
assumptions (4)
  • domain assumption Vertex positions follow univariate Laplace distributions per coordinate
    Used in Eq. 3 following Gu et al.; the uncertainty model is assumed rather than derived from data or physical principles.
  • ad hoc to paper Category confidence of prior queries separates matched from outdated elements
    Sec. 3.3 states 'we believe that using the confidence score... can effectively distinguish between them' without quantitative validation.
  • domain assumption Synthetic fake elements and element removal during training approximate real map changes
    Sec. 4.1 uses random removal and fake generation because public datasets lack sufficient real change events.
  • domain assumption Evaluation ground truth from aligning validation set with sensors is reliable
    Sec. 4.2 augments map data by aligning the validation set with sensor data, which could introduce bias or noise into the reference.

how reviews work

0 comments
Cite this review

Pith. "Pith review of RTMap: Real-Time Recursive Mapping with Change Detection and Localization." pith.science (2026). https://pith.science/paper/V322DQXO

@misc{pith2026250700980,
  author       = {Pith},
  title        = {Pith review of: RTMap: Real-Time Recursive Mapping with Change Detection and Localization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V322DQXO}},
  note         = {Machine review of arXiv:2507.00980}
}
read the original abstract

While recent online HD mapping methods relieve burdened offline pipelines and solve map freshness, they remain limited by perceptual inaccuracies, occlusion in dense traffic, and an inability to fuse multi-agent observations. We propose RTMap to enhance these single-traversal methods by persistently crowdsourcing a multi-traversal HD map as a self-evolutional memory. On onboard agents, RTMap simultaneously addresses three core challenges in an end-to-end fashion: (1) Uncertainty-aware positional modeling for HD map elements, (2) probabilistic-aware localization w.r.t. the crowdsourced prior-map, and (3) real-time detection for possible road structural changes. Experiments on several public autonomous driving datasets demonstrate our solid performance on both the prior-aided map quality and the localization accuracy, demonstrating our effectiveness of robustly serving downstream prediction and planning modules while gradually improving the accuracy and freshness of the crowdsourced prior-map asynchronously. Our source-code will be made publicly available at https://github.com/CN-ADLab/RTMap.

Figures

Figures reproduced from arXiv: 2507.00980 by the authors.

Figure 1
Figure 1. RTMap performs real-time online HD mapping on onboard driving agents, simultaneously solving map-based localization, map [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Online and offline modules of RTMap. We encode sensors and the crowdsourced HD map to perform hybrid queries and [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Trend of queries in different decoder layers. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of the localization performance [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparison trending the quality improvement through solving a precise pose for alignment, and a multi-traversal [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

46 extracted references · 41 canonical work pages

  1. [1]

    Exploring real world map change generalization of prior- informed hd map prediction models

    Samuel M Bateman, Ning Xu, H Charles Zhao, Yael Ben Shalom, Vince Gong, Greg Long, and Will Maddern. Exploring real world map change generalization of prior- informed hd map prediction models. In CVPR, pages 4568– 4578, 2024. 2

    work page 2024

  2. [2]

    Bowman, Nikolay Atanasov, Kostas Daniilidis, and George J

    Sean L. Bowman, Nikolay Atanasov, Kostas Daniilidis, and George J. Pappas. Probabilistic data association for semantic SLAM. In ICRA, pages 1722–1729, 2017. 3

    work page 2017

  3. [3]

    nuScenes: A mul- timodal dataset for autonomous driving

    Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuScenes: A mul- timodal dataset for autonomous driving. In CVPR, pages 11621–11631, 2020. 2, 6, 7

    work page 2020

  4. [4]

    U-bev: Height- aware bird’s-eye-view segmentation and neural map-based relocalization

    Andrea Boscolo Camiletto, Alfredo Bochicchio, Alexander Liniger, Dengxin Dai, and Abel Gawel. U-bev: Height- aware bird’s-eye-view segmentation and neural map-based relocalization. In IROS, pages 5597–5604, 2024. 2

    work page 2024

  5. [5]

    Real-time high- accuracy three-dimensional reconstruction with consumer rgb-d cameras

    Yan-Pei Cao, Leif Kobbelt, and Shi-Min Hu. Real-time high- accuracy three-dimensional reconstruction with consumer rgb-d cameras. ACM TOG, 37(5):171:1–171:16, 2018. 3, 5

    work page 2018

  6. [6]

    Maptracker: Tracking with strided memory fusion for consistent vector hd mapping

    Jiacheng Chen, Yuefan Wu, Jiaqi Tan, Hang Ma, and Yasu- taka Furukawa. Maptracker: Tracking with strided memory fusion for consistent vector hd mapping. In ECCV, pages 90–107, 2024. 2

    work page 2024

  7. [7]

    Online temporal fusion for vectorized map construction in mapless autonomous driving

    Jiagang Chen, Liangliang Pan, Shunping Ji, Ji Zhao, and Zichao Zhang. Online temporal fusion for vectorized map construction in mapless autonomous driving. IEEE Robotics and Automation Letters, 10(4):3948–3955, 2025. 2

    work page 2025

  8. [8]

    Efficient and robust 2d-to-bev representation learning via geometry-guided ker- nel transformer

    Shaoyu Chen, Tianheng Cheng, Xinggang Wang, Wenming Meng, Qian Zhang, and Wenyu Liu. Efficient and robust 2d-to-bev representation learning via geometry-guided ker- nel transformer. arXiv:2206.04584, 2022. 2

    arXiv 2022

Show all 46 references
  1. [9]

    Road mapping and local- ization using sparse semantic visual features

    Wentao Cheng, Sheng Yang, Maomin Zhou, Ziyuan Liu, Yiming Chen, and Mingyang Li. Road mapping and local- ization using sparse semantic visual features. IEEE Robotics and Automation Letters, 6(4):8118–8125, 2021. 3

    2021

  2. [10]

    Accurate dynamic slam using crf-based long-term consistency

    Zheng-Jun Du, Shi-Sheng Huang, Tai-Jiang Mu, Qunhe Zhao, Ralph R Martin, and Kun Xu. Accurate dynamic slam using crf-based long-term consistency. IEEE TVCG, 28(4): 1745–1757, 2020. 3

    2020

  3. [11]

    High-definition maps: Comprehensive survey, chal- lenges and future perspectives

    Gamal Elghazaly, Rapha ¨el Frank, Scott Harvey, and Stefan Safko. High-definition maps: Comprehensive survey, chal- lenges and future perspectives. IEEE Open Journal of Intel- ligent Transportation Systems, 4:527–550, 2023. 2

    2023

  4. [12]

    On-manifold preintegration for real-time visual-inertial odometry

    Christian Forster, Luca Carlone, Frank Dellaert, and Da- vide Scaramuzza. On-manifold preintegration for real-time visual-inertial odometry. IEEE Transactions on Robotics, 33 (1):1–21, 2016. 5

    2016

  5. [13]

    Producing and leveraging on- line map uncertainty in trajectory prediction

    Xunjiang Gu, Guanyu Song, Igor Gilitschenski, Marco Pavone, and Boris Ivanovic. Producing and leveraging on- line map uncertainty in trajectory prediction. InCVPR, pages 14521–14530, 2024. 3, 4

    2024

  6. [14]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770–778, 2016. 5

    2016

  7. [15]

    Egovm: Achieving precise ego-localization using lightweight vectorized maps

    Yuzhe He, Shuang Liang, Xiaofei Rui, Chengying Cai, and Guowei Wan. Egovm: Achieving precise ego-localization using lightweight vectorized maps. In IROS, pages 12248– 12255, 2024. 2, 3, 7

    2024

  8. [16]

    Lidar-monocular visual odometry using point and line features

    Shi-Sheng Huang, Ze-Yu Ma, Tai-Jiang Mu, Hongbo Fu, and Shi-Min Hu. Lidar-monocular visual odometry using point and line features. In ICRA, pages 1091–1097, 2020. 3

    2020

  9. [17]

    P-MapNet: Far-seeing map generator enhanced by both sdmap and hdmap priors

    Zhou Jiang, Zhenxin Zhu, Pengfei Li, Huan-ang Gao, Tianyuan Yuan, Yongliang Shi, Hang Zhao, and Hao Zhao. P-MapNet: Far-seeing map generator enhanced by both sdmap and hdmap priors. In ICLR, pages 1–12, 2024. 2

    2024

  10. [18]

    Trust, but verify: Cross- modality fusion for hd map change detection

    John Lambert and James Hays. Trust, but verify: Cross- modality fusion for hd map change detection. In NeurIPS, pages 1–14, 2021. 2, 6, 7

    2021

  11. [19]

    Lidar odometry survey: recent advancements and re- maining challenges

    Dongjae Lee, Minwoo Jung, Wooseong Yang, and Ayoung Kim. Lidar odometry survey: recent advancements and re- maining challenges. Intelligent Service Robotics, 17(2):95– 118, 2024. 3

    2024

  12. [20]

    Hdmapnet: An online hd map construction and evaluation framework

    Qi Li, Yue Wang, Yilun Wang, and Hang Zhao. Hdmapnet: An online hd map construction and evaluation framework. In ICRA, pages 4628–4634, 2022. 2

    2022

  13. [21]

    DTCLMapper: Dual temporal consistent learning for vectorized hd map construc- tion

    Siyu Li, Jiacheng Lin, Hao Shi, Jiaming Zhang, Song Wang, You Yao, Zhiyong Li, and Kailun Yang. DTCLMapper: Dual temporal consistent learning for vectorized hd map construc- tion. IEEE Transactions on Intelligent Transportation Sys- tems, 25(12):21672–21686, 2024. 2

    2024

  14. [22]

    LaneSegNet: Map learning with lane segment perception for autonomous driving

    Tianyu Li, Peijin Jia, Bangjun Wang, Li Chen, Kun Jiang, Junchi Yan, and Hongyang Li. LaneSegNet: Map learning with lane segment perception for autonomous driving. In ICLR, 2024. 2

    2024

  15. [23]

    BEVFormer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers

    Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chong- hao Sima, Tong Lu, Yu Qiao, and Jifeng Dai. BEVFormer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers. In ECCV, pages 1– 18, 2022. 2

    2022

  16. [24]

    MapTR: Structured modeling and learning for online vectorized hd map construction

    Bencheng Liao, Shaoyu Chen, Xinggang Wang, Tianheng Cheng, Qian Zhang, Wenyu Liu, and Chang Huang. MapTR: Structured modeling and learning for online vectorized hd map construction. In ICLR, pages 1–18, 2023. 1, 2, 4

    2023

  17. [25]

    MapTRv2: An end-to-end framework for online vectorized hd map construction

    Bencheng Liao, Shaoyu Chen, Yunchi Zhang, Bo Jiang, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang. MapTRv2: An end-to-end framework for online vectorized hd map construction. International Journal of Computer Vi- sion, 133(3):1352–1374, 2025. 2, 5, 6

    2025

  18. [26]

    MGMap: Mask-guided learning for online vectorized hd map construction

    Xiaolu Liu, Song Wang, Wentong Li, Ruizi Yang, Junbo Chen, and Jianke Zhu. MGMap: Mask-guided learning for online vectorized hd map construction. In CVPR, pages 14812–14821, 2024. 1, 2

    2024

  19. [27]

    VectorMapNet: End-to-end vectorized hd map learning

    Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, and Hang Zhao. VectorMapNet: End-to-end vectorized hd map learning. In ICML, pages 22352–22369, 2023. 2

    2023

  20. [28]

    Aug- menting lane perception and topology understanding with standard definition navigation maps

    Katie Z Luo, Xinshuo Weng, Yan Wang, Shuang Wu, Jie Li, Kilian Q Weinberger, Yue Wang, and Marco Pavone. Aug- menting lane perception and topology understanding with standard definition navigation maps. In ICRA, pages 4029– 4035, 2024. 2

    2024

  21. [29]

    A survey on monocular re- localization: From the perspective of scene map represen- tation

    Jinyu Miao, Kun Jiang, Tuopu Wen, Yunlong Wang, Pei- jin Jia, Benny Wijaya, Xuhe Zhao, Qian Cheng, Zhongyang Xiao, Jin Huang, et al. A survey on monocular re- localization: From the perspective of scene map represen- tation. IEEE TIV, pages 1–33, 2024. 3

    2024

  22. [30]

    Prevpredmap: Explor- ing temporal modeling with previous predictions for online vectorized hd map construction

    Nan Peng, Xun Zhou, Mingming Wang, Xiaojun Yang, Songming Chen, and Guisong Chen. Prevpredmap: Explor- ing temporal modeling with previous predictions for online vectorized hd map construction. In WACV, pages 8134– 8143, 2025. 2

    2025

  23. [31]

    Vins-mono: A robust and versatile monocular visual-inertial state estimator

    Tong Qin, Peiliang Li, and Shaojie Shen. Vins-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics, 34(4):1004–1020, 2018. 5

    2018

  24. [32]

    Crowd-sourced nerf: Col- lecting data from production vehicles for 3d street view re- construction

    Tong Qin, Changze Li, Haoyang Ye, Shaowei Wan, Minzhen Li, Hongwei Liu, and Ming Yang. Crowd-sourced nerf: Col- lecting data from production vehicles for 3d street view re- construction. IEEE Transactions on Intelligent Transporta- tion Systems, 25(11):16145–16156, 2024. 2

    2024

  25. [33]

    Mind the map! accounting for existing map information when estimating online hdmaps from sensor data

    R ´emy Sun, Li Yang, Diane Lingrand, and Fr´ed´eric Precioso. Mind the map! accounting for existing map information when estimating online hdmaps from sensor data. In WACV, pages 1–11, 2025. 2, 4

    2025

  26. [34]

    THMA: Tencent hd map ai system for creating hd map annotations

    Kun Tang, Xu Cao, Zhipeng Cao, Tong Zhou, Erlong Li, Ao Liu, Shengtao Zou, Chang Liu, Shuqi Mei, Elena Sizikova, et al. THMA: Tencent hd map ai system for creating hd map annotations. In AAAI, pages 15585–15593, 2023. 2

    2023

  27. [35]

    High definition map mapping and update: A general overview and future directions

    Benny Wijaya, Kun Jiang, Mengmeng Yang, Tuopu Wen, Yunlong Wang, Xuewei Tang, Zheng Fu, Taohua Zhou, and Diange Yang. High definition map mapping and update: A general overview and future directions. arXiv:2409.09726,

    arXiv

  28. [36]

    Exelmap: Explainable element-based hd-map change detection and update

    Lena Wild, Ludvig Ericson, Rafael Valencia, and Patric Jens- felt. Exelmap: Explainable element-based hd-map change detection and update. arXiv:2409.10178, 2024. 3

    2024 arXiv

  29. [37]

    MapNeRF: Incorporating map priors into neural ra- diance fields for driving view simulation

    Chenming Wu, Jiadai Sun, Zhelun Shen, and Liangjun Zhang. MapNeRF: Incorporating map priors into neural ra- diance fields for driving view simulation. In IROS, pages 7082–7088, 2023. 2

    2023

  30. [38]

    LDMapNet-U: An end-to-end system for city-scale lane-level map updating

    Deguo Xia, Weiming Zhang, Xiyan Liu, Wei Zhang, Chent- ing Gong, Xiao Tan, Jizhou Huang, Mengmeng Yang, and Diange Yang. LDMapNet-U: An end-to-end system for city-scale lane-level map updating. In ACM SIGKDD, page 2693–2702, 2025. 3

    2025

  31. [39]

    Neural map prior for autonomous driving

    Xuan Xiong, Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, and Hang Zhao. Neural map prior for autonomous driving. In CVPR, pages 17535–17544, 2023. 2

    2023

  32. [40]

    A robust pose graph approach for city scale LiDAR mapping

    Sheng Yang, Xiaoling Zhu, Xing Nian, Lu Feng, Xiaozhi Qu, and Teng Ma. A robust pose graph approach for city scale LiDAR mapping. In IROS, pages 1175–1182, 2018. 2

    2018

  33. [41]

    StreamMapNet: Streaming mapping network for vectorized online hd map construction

    Tianyuan Yuan, Yicheng Liu, Yue Wang, Yilun Wang, and Hang Zhao. StreamMapNet: Streaming mapping network for vectorized online hd map construction. In WACV, pages 7341–7350, 2024. 2

    2024

  34. [42]

    Driving with prior maps: Unified vector prior encoding for autonomous vehicle mapping

    Shuang Zeng, Xinyuan Chang, Xinran Liu, Zheng Pan, and Xing Wei. Driving with prior maps: Unified vector prior encoding for autonomous vehicle mapping. arXiv preprint arXiv:2409.05352, 2024. 2

    2024

  35. [43]

    Enhancing vectorized map perception with historical rasterized maps

    Xiaoyu Zhang, Guangwei Liu, Zihao Liu, Ningyi Xu, Yun- hui Liu, and Ji Zhao. Enhancing vectorized map perception with historical rasterized maps. In European Conference on Computer Vision, pages 422–439. Springer, 2024. 6

    2024

  36. [44]

    Bev-locator: An end-to-end vi- sual semantic localization network using multi-view images

    Zhihuang Zhang, Meng Xu, Wenqiang Zhou, Tao Peng, Liang Li, and Stefan Poslad. Bev-locator: An end-to-end vi- sual semantic localization network using multi-view images. Science China Information Sciences, 68(2):122106, 2025. 3, 7

    2025

  37. [45]

    L-VIWO: Visual-inertial-wheel odometry based on lane lines

    Bin Zhao, Yunzhou Zhang, Junjie Huang, Xichen Zhang, Zeyu Long, and Yulong Li. L-VIWO: Visual-inertial-wheel odometry based on lane lines. In ICRA, pages 18079–18085,

  38. [46]

    GP-Recon: Online monocular neural 3d reconstruction with geometric prior

    Zi-Xin Zou, Shi-Sheng Huang, Yan-Pei Cao, Tai-Jiang Mu, Ying Shan, Hongbo Fu, and Song-Hai Zhang. GP-Recon: Online monocular neural 3d reconstruction with geometric prior. IEEE TVCG, 2024. 3

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.