Pith. sign in

REVIEW 3 major objections 6 minor 176 references

4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception

T0 review · 3 major / 6 minor · reviewed 2026-07-14 · grok-4.5

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

Pith's one-line read Semantic occupancy works better as a persistent intermediate scene state that guides 4D radar-camera features before boxes and voxels are decoded.

desk verdict Solid systems paper: occupancy as intermediate state for 4D radar-camera dual-task perception, with real dual-dataset gains; ManTruckScenes labels are the softest piece, not the whole case. read the letter →

arxiv 2607.09629 v2 pith:PE6DWO43 submitted 2026-07-10 cs.CV cs.AI

Xiaokai Bai , Lianqing Zheng , Runwei Guan , Songkai Wang , Siyuan Cao , Hui-liang Shen This is my paper
classification cs.CVcs.AI
keywords 4Dradarradar-camerafusion3Dobjectdetectionsemanticoccupancybird's-eyeviewmulti-taskperceptiontemporalautonomousdriving
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

Reliable full-scene driving perception must jointly describe moving objects and the dense layout around them. Most 4D radar-camera systems either chase detection alone or attach occupancy as a late side branch after a shared bird’s-eye-view feature is already fixed. This paper claims that occupancy should instead be predicted early and kept as a living scene state that steers feature refinement inside a frame and across time. State-guided BEV Enhancement uses non-empty occupancy confidence to focus current-frame attention, while Doppler-guided Temporal Fusion warps and fuses history under the same state using radar motion. The authors also generate occupancy labels for a second commercial-vehicle dataset so the idea can be tested under one dual-task protocol on two multi-view radar-camera benchmarks. A sympathetic reader cares because sparse but weather-stable radar can then shape dense layout and object reasoning before final decoding, not only after.

What carries the argument

Cross-modal state reasoning, carried by State-guided BEV Enhancement (SBE) and Doppler-guided Temporal Fusion (DTF). SBE predicts occupancy logits before attention and uses continuous non-empty confidence plus depth-aware image context to refine the current BEV; DTF indexes non-empty historical voxels, ego-aligns them, corrects motion with radar Doppler, and fuses the warped memory into a shared state for both heads.

What would settle it

Re-evaluate the same models on ManTruckScenes with independently measured dense occupancy (for example multi-frame LiDAR semantic completion without satellite priors). If the reported margins over strong dual-head baselines collapse while OmniHD-Scenes gains remain, the cross-dataset state-reasoning claim fails.

Watch

Extended reading notes

Core claim

In 360° 4D radar-camera multi-task perception, semantic occupancy should be modeled as a persistent intermediate scene state rather than a terminal output. When that state is predicted coarse-to-fine, used to refine bird’s-eye-view features, and propagated over time with Doppler-corrected motion, joint 3D detection and semantic occupancy both improve on OmniHD-Scenes and on ManTruckScenes under a unified dual-task evaluation protocol.

Load-bearing premise

The satellite-map occupancy labels built for ManTruckScenes are faithful enough that dual-task gains on that dataset reflect real scene understanding rather than artifacts of how those labels were generated.

Editorial extensions

If this is right

  • Dual-head radar-camera designs that decode occupancy only after shared BEV fusion leave measurable accuracy on both boxes and voxels.
  • Occupancy confidence can act as a continuous spatial prior for attention and temporal retrieval instead of a hard mask or late branch.
  • Radar Doppler becomes useful multi-task memory when it corrects velocity only on state-selected non-empty regions.
  • A second multi-view radar-camera occupancy benchmark makes dual-task claims testable beyond a single dataset contract.
  • Night and rain scenes still benefit when radar-stable geometry remains inside the shared state rather than only in a detector head.

Reading between the lines

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

  • If occupancy-as-state is the right interface, the same intermediate state could later feed planning or vision-language-action stacks without rebuilding layout from boxes alone.
  • Satellite-anchored generated labels may bias thin static classes, so pure-camera occupancy methods could look artificially strong or weak on ManTruckScenes relative to true dense LiDAR occupancy.
  • State-indexed temporal fusion might transfer to camera-only multi-task systems if a learned occupancy prior can replace Doppler for motion gating.
  • Cross-dataset dual-task protocols may become expected reporting practice once more radar-camera occupancy exports exist.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper proposes 4DR360°, a multi-view 4D radar–camera framework for joint 360° 3D detection and semantic occupancy. Its central claim is that occupancy should be treated as a persistent intermediate scene state, not only a terminal head: after radar–camera BEV fusion, a coarse-to-fine voxel hierarchy predicts occupancy logits that guide State-guided BEV Enhancement (SBE) via non-empty confidence and depth-aware cross-attention, then Doppler-guided Temporal Fusion (DTF) retrieves and warps historical state with ego alignment, occupancy-indexed support, and radar Doppler correction before joint decoding. The authors also generate satellite-map-based occupancy labels for ManTruckScenes and evaluate under a shared MMDetection3D dual-task protocol with OmniHD-Scenes. Tables 1–4 report consistent dual-task gains over Doracamom and strong detection baselines; Table 5 shows adverse-condition gains; Tables 6–8 ablate state, SBE, temporal horizon, and Doppler.

Significance. If the gains hold under fair dual-task comparison, the paper is a useful step for 4D radar–camera full-scene perception: it moves beyond detection-only BEV fusion and dual-head attachment by making occupancy an explicit intermediate reasoning interface, and it couples that interface to radar Doppler motion. Strengths include a clear module decomposition (SBE/DTF), multi-scale state supervision, a shared evaluation contract across methods, dual-dataset reporting, adverse-condition evaluation, and ablations that separate state memory from generic history fusion. The promised release of code and ManTruckScenes occupancy labels would further strengthen the contribution as a benchmark extension for a still-narrow multi-task radar–camera setting.

major comments (3)
  1. ManTruckScenes Occupancy Construction / Fig. 3 and Tables 2, 4, 6–8: the dual-task and ablation story leans heavily on generated occupancy (LiDAR static anchors, conservative satellite semantics, object-local surface replay). These targets can systematically favor methods that reuse LiDAR-anchored geometry, box-local dynamics, and Doppler motion—the same cues SBE/DTF exploit—so reported mIoU/NDS gains and the DTF ablations may partly measure agreement with the construction pipeline. OmniHD-Scenes native results (Tables 1, 3, 5) already support the claim, but the paper should quantify label fidelity (e.g., held-out LiDAR/manual audit metrics, class-wise error modes, sensitivity of rankings to label variants) and state more carefully which conclusions are ManTruck-dependent versus OmniHD-supported.
  2. Experimental Setup and Tables 1–4: several strong baselines (SGDet3D, Doracamom, RCBEVDet-family) and both datasets are closely related to the authors’ prior line. The shared MMDetection3D contract is good practice, but the manuscript should document more explicitly which baseline components were reimplemented, which pretrained weights/hyperparameters were reused, and whether dual-task heads for detection-only methods were trained under identical occupancy supervision budgets. Without that, the magnitude of gains over Doracamom/HGSFusion is harder to interpret as purely architectural.
  3. Method, Eqs. (3)–(6) and (7)–(11): the paper claims occupancy is a reusable intermediate state that improves object reasoning before decoding, yet the main text gives limited evidence that SBE/DTF change detection features in a causal way beyond multi-task co-training. Table 6 shows cumulative gains, but a load-bearing check is missing: e.g., freeze or detach occupancy gradients into the detector path, or report detection-only metrics when occupancy state is predicted but not fed back. Without such a control, ‘state reasoning’ remains partly confounded with denser multi-scale occupancy supervision.
minor comments (6)
  1. Notation consistency: the abstract/title use 4DR360 / 4DR360°, while body text mixes 4DR360◦; unify the method name and degree symbol.
  2. Eq. (2) and surrounding text: the height-lift / reshape notation is dense; a short schematic of channel adaptation, height MLP, and multi-scale indices would help readers implement the state hierarchy.
  3. Tables 1–2 report FPS for many methods, but efficiency discussion is deferred to the supplement; a brief main-text note on parameter/FLOP overhead of SBE+DTF versus Doracamom would better support the practicality claim.
  4. Figure 5 qualitative results are useful but hard to inspect at manuscript scale; consider larger crops or failure cases (thin obstacles, night/rain) aligned with Table 5.
  5. Related Work: UniVision/SOGDet are cited for occupancy-guided detection; a clearer sentence on why their occupancy-to-detection coupling does not already cover the radar Doppler + multi-scale state design would sharpen novelty.
  6. Training Objective Eq. (12): define the geometric/semantic scaling losses more explicitly in the main text or point to exact supplement equations; ‘geo/sem scaling’ is currently opaque.

Circularity Check

1 steps flagged · score 1.0 of 10

Empirical multi-task radar-camera paper; gains are measured against external and author-line baselines, not forced by definition or fit.

  1. self citation load bearing [Related Work; ManTruckScenes Occupancy Construction; Tables 2,4,6–8]
    "OmniHD-Scenes is currently the only public 4D radar-camera benchmark with both 3D detection and semantic occupancy annotations (Zheng et al. 2024), and Doracamom provides a joint reference on this setting (Zheng et al. 2026). ... We therefore extend ManTruckScenes with an auditable occupancy export. ... All ablations are built on the adapted RCBEVDet baseline."

    Not true circularity of the main claim, but the dual-task protocol and strongest ablations sit partly inside an author-overlapping ecosystem (OmniHD/Doracamom/SGDet3D lineage plus author-built ManTruckScenes occupancy). This is mild self-lineage, not a uniqueness theorem or by-construction prediction; OmniHD-native Tables 1/3/5 and external baselines still provide independent empirical checks.

full rationale

4DR360° is an engineering/empirical perception paper, not a first-principles derivation. Its central claim—that treating semantic occupancy as an intermediate scene state (via SBE and DTF) improves joint detection and occupancy—is supported by reported metric gains on OmniHD-Scenes (native labels) and ManTruckScenes (author-generated labels), plus component ablations. Those outcomes are not equivalent to the inputs by construction: the network could have underperformed Doracamom, RCBEVDet, BEVFusion, HGSFusion, etc. Intermediate occupancy supervision and state-guided attention are design choices with multi-scale losses, not tautological redefinitions of the evaluation targets. Author overlap with SGDet3D, Doracamom, OmniHD-Scenes, and the ManTruckScenes occupancy export is real research-line self-citation, but it is not load-bearing uniqueness or a fitted parameter renamed as prediction; external baselines and OmniHD-native results still carry independent falsifiability. Label-construction bias on ManTruckScenes is a validity concern, not circular derivation. Score 1 only for mild self-lineage risk that does not collapse the claim.

Assumptions & free parameters 5 free parameters · 6 assumptions · 4 invented entities

This is an empirical systems paper. The load-bearing premises are standard multi-task BEV fusion assumptions, the claim that intermediate occupancy state is useful before decoding, Doppler/motion cues as temporal guidance, and especially the fidelity of generated ManTruckScenes occupancy labels. Free parameters are ordinary training/design choices rather than physics constants; invented entities are architectural modules, not new physical objects.

free parameters (5)
  • Temporal horizon T_h
    History length is a design choice; Table 7 shows 4-frame DTF best among tested settings, so reported gains depend on this selection.
  • Loss weights lambda_depth, lambda, and stage weights w_i
    Multi-task and multi-stage occupancy losses are weighted; exact values are not fully specified in the main text but affect the joint optimum.
  • Displacement clip threshold tau
    DTF clips large motion shifts; this hand-chosen limit affects dynamic warping reliability.
  • Voxel grid and range (160x240x16, shared point-cloud range)
    Occupancy and detection metrics depend on this discretization contract shared across methods.
  • Training schedule (AdamW, 16 epochs, batch/augmentation contract)
    All compared systems are trained under one schedule; absolute numbers are schedule-dependent.
assumptions (6)
  • domain assumption Camera appearance plus sparse 4D radar geometry/Doppler are complementary enough that BEV fusion can support both boxes and dense semantics.
    Stated throughout Introduction and Feature Extractor; standard radar-camera premise.
  • ad hoc to paper Occupancy logits can serve as a reusable intermediate state that should guide feature refinement before final decoding rather than only as a terminal head.
    Core design premise of Cross-modal State Representation and SBE.
  • ad hoc to paper Non-empty occupancy confidence M(O)=1-softmax(O)[..., empty] is a valid continuous interface for attention, voxel recovery, and temporal memory selection.
    Defined in Cross-modal State Representation and used in Eqs. for SBE/DTF.
  • domain assumption Radar Doppler plus decoded box velocity can correct dynamic warping better than ego alignment alone.
    Load-bearing for DTF; tested in Table 8.
  • ad hoc to paper Generated ManTruckScenes occupancy from LiDAR anchors, satellite semantics, and object-local replay is sufficiently accurate for dual-task training and ranking methods.
    ManTruckScenes Occupancy Construction and Fig. 3; underpins cross-dataset claims.
  • domain assumption Adapting baselines with task-minimal heads under one MMDetection3D contract yields fair multi-task comparison.
    Experimental Setup claim-evidence alignment paragraph.
invented entities (4)
  • Occupancy-as-persistent-scene-state representation
    purpose: Make dense layout an intermediate reasoning interface that shapes BEV/voxel features before detection and occupancy decoding.
    Central conceptual object of the paper; evaluated only through downstream metrics, not independent physical measurement.
  • State-guided BEV Enhancement (SBE)
    purpose: Use predicted occupancy confidence and depth-aware image context to refine current-frame BEV features via guided self/cross attention.
    New module; evidence is ablation gains, not external corroboration.
  • Doppler-guided Temporal Fusion (DTF)
    purpose: Retrieve and warp historical state with occupancy support and Doppler-corrected motion before temporal fusion.
    New module; evidence is temporal/Doppler ablations.
  • Satellite-map-based ManTruckScenes occupancy labels
    purpose: Create a second dual-task radar-camera benchmark contract beyond OmniHD-Scenes.
    New dataset artifact; quality depends on generation pipeline and audit described by the authors.

how reviews work

0 comments
Cite this review

Pith. "Pith review of 4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception." pith.science (2026). https://pith.science/paper/PE6DWO43

@misc{pith2026260709629,
  author       = {Pith},
  title        = {Pith review of: 4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PE6DWO43}},
  note         = {Machine review of arXiv:2607.09629}
}
abstract

Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout. Recently, 4D millimeter-wave radar has emerged as a robust and affordable sensor, yet its sparse returns make radar-camera fusion necessary for comprehensive scene understanding. Existing radar-camera methods mainly optimize detection, while dual-task systems usually decode boxes and occupancy with limited interaction. To address this gap and advance radar-based multi-task learning, we propose \method, a 4D radar-camera framework for 360$^\circ$ full-scene perception, which models semantic occupancy as a persistent scene state rather than a terminal output. \method{} follows a cross-modal state reasoning paradigm, where the occupancy state is modeled and propagated through stages for coarse-to-fine feature aggregation. Specifically, State-guided BEV Enhancement (SBE) strengthens intra-frame BEV representation, while Doppler-guided Temporal Fusion (DTF) preserves state evidence over longer temporal horizons. Beyond the model, we further extend ManTruckScenes with satellite-map-based generated occupancy labels and pair it with OmniHD-Scenes in a unified cross-dataset detection-and-occupancy protocol. The resulting experiments cover accuracy, robustness, ablation, and efficiency under one radar-camera multi-task evaluation framework. Code and labels will be released upon acceptance.

Figures

Figures reproduced from arXiv: 2607.09629 by the authors.

Figure 1
Figure 1. Comparison of representative 4D radar-camera [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overall framework of 4DR360◦ . The pipeline first extracts multi-view image BEV features and 4D radar BEV features, fuses them into a shared BEV representation, reasons over coarse-to-fine occupancy states with SBE and DTF, and decodes joint 3D detection and semantic occupancy outputs. while a key limitation remains. SGDet3D (Bai et al. 2024) focuses on front-view detection. RCBEVDet (Lin et al. 2024) extends radar-… view at source ↗
Figure 3
Figure 3. ManTruckScenes occupancy construction. LiDAR [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: State reasoning modules. SBE predicts occupancy [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Qualitative results on OmniHD-Scenes (left) and ManTruckScenes (right). [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Review history (2 revisions) →

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

176 extracted references · 2 linked inside Pith

  1. [1]

    Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

    Clancey, William J. Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83)

  2. [2]

    Classification Problem Solving

    Clancey, William J. Classification Problem Solving. Proceedings of the Fourth National Conference on Artificial Intelligence

  3. [3]

    , title =

    Robinson, Arthur L. , title =. 1980 , doi =. https://science.sciencemag.org/content/208/4447/1019.full.pdf , journal =

    1980

  4. [4]

    New Ways to Make Microcircuits Smaller---Duplicate Entry

    Robinson, Arthur L. New Ways to Make Microcircuits Smaller---Duplicate Entry. Science

  5. [5]

    Clancey and Glenn Rennels , abstract =

    Diane Warner Hasling and William J. Clancey and Glenn Rennels , abstract =. Strategic explanations for a diagnostic consultation system , journal =. 1984 , issn =. doi:https://doi.org/10.1016/S0020-7373(84)80003-6 , url =

    doi:10.1016/s0020-7373(84)80003-6 1984

  6. [6]

    and Rennels, Glenn R

    Hasling, Diane Warner and Clancey, William J. and Rennels, Glenn R. and Test, Thomas. Strategic Explanations in Consultation---Duplicate. The International Journal of Man-Machine Studies

  7. [7]

    Poligon: A System for Parallel Problem Solving

    Rice, James. Poligon: A System for Parallel Problem Solving

  8. [8]

    Transfer of Rule-Based Expertise through a Tutorial Dialogue

    Clancey, William J. Transfer of Rule-Based Expertise through a Tutorial Dialogue

Show all 176 references
  1. [9]

    The Engineering of Qualitative Models

    Clancey, William J. The Engineering of Qualitative Models

  2. [10]

    2017 , eprint=

    Attention Is All You Need , author=. 2017 , eprint=

    2017

  3. [11]

    Pluto: The 'Other' Red Planet

    NASA. Pluto: The 'Other' Red Planet

  4. [12]

    Dreissig, Mariella and Scheuble, Dominik and Piewak, Florian and Boedecker, Joschka , booktitle=

  5. [13]

    Li, Yingwei and Yu, Adams Wei and Meng, Tianjian and Caine, Ben and Ngiam, Jiquan and Peng, Daiyi and Shen, Junyang and Lu, Yifeng and Zhou, Denny and Le, Quoc V and others , booktitle=

  6. [14]

    Bai, Xuyang and Hu, Zeyu and Zhu, Xinge and Huang, Qingqiu and Chen, Yilun and Fu, Hongbo and Tai, Chiew-Lan , booktitle=

  7. [15]

    2024 , volume=

    Fan, Lili and Wang, Junhao and Chang, Yuanmeng and Li, Yuke and Wang, Yutong and Cao, Dongpu , journal=. 2024 , volume=

    2024

  8. [16]

    Wu, Xiaopei and Peng, Liang and Yang, Honghui and Xie, Liang and Huang, Chenxi and Deng, Chengqi and Liu, Haifeng and Cai, Deng , booktitle=

  9. [17]

    Zhang, Yifan and Hu, Qingyong and Xu, Guoquan and Ma, Yanxin and Wan, Jianwei and Guo, Yulan , booktitle=

  10. [18]

    2020 , publisher=

    Mohammed, Abdul Sajeed and Amamou, Ali and Ayevide, Follivi Kloutse and Kelouwani, Sousso and Agbossou, Kodjo and Zioui, Nadjet , journal=. 2020 , publisher=

    2020

  11. [19]

    arXiv preprint 2411.15016 , archivePrefix=

    Hongsi Liu and Jun Liu and Guangfeng Jiang and Xin Jin , year=. arXiv preprint 2411.15016 , archivePrefix=

  12. [20]

    Zhao, Haocheng and Guan, Runwei and Wu, Taoyu and Man, Ka Lok and Yu, Limin and Yue, Yutao , journal=

  13. [21]

    Wolters, Philipp and Gilg, Johannes and Teepe, Torben and Herzog, Fabian and Laouichi, Anouar and Hofmann, Martin and Rigoll, Gerhard , journal=

  14. [22]

    2024 , volume=

    Bai, Xiaokai and Yu, Zhu and Zheng, Lianqing and Zhang, Xiaohan and Zhou, Zili and Zhang, Xue and Wang, Fang and Bai, Jie and Shen, Hui-Liang , journal=. 2024 , volume=

    2024

  15. [23]

    Bai, Xiaokai and Zhou, Chenxu and Zheng, Lianqing and Cao, Si-Yuan and Liu, Jianan and Zhang, Xiaohan and Li, Yiming and Zhang, Zhengzhuang and Shen, Hui-Liang , booktitle=

  16. [24]

    Xia, Zhongyu and Tang, Yousen and Wang, Yongtao and Wang, Zhifeng and Qin, Weijun , journal=

  17. [25]

    Bai, Xiaokai and Yang, Qin and Zhou, Zili and Zhang, Fuyi and Wu, Zhe and Cao, Si-Yuan and Zheng, Lianqing and Yu, Beinan and Wang, Fang and Bai, Jie and Shen, Hui-Liang , booktitle=

  18. [26]

    Bai, Xiaokai and Cheng, Jiahao and Wang, Songkai and Luo, Yixuan and Zheng, Lianqing and Zhang, Xiaohan and Cao, Si-Yuan and Shen, Hui-Liang , booktitle=

  19. [27]

    Guan, Runwei and Liu, Jianan and Liang, Shaofeng and Ding, Fangqiang and Yao, Shanliang and Bai, Xiaokai and Liu, Daizong and Huang, Tao and Mao, Guoqiang and Xiong, Hui , journal=

  20. [28]

    Zhang, Fuyi and Yu, Zhu and Li, Chunhao and Zhang, Runmin and Bai, Xiaokai and Zhou, Zili and Cao, Si-Yuan and Wang, Fang and Shen, Hui-Liang , booktitle=

  21. [29]

    Bai, Xiaokai and Zheng, Lianqing and Cao, Si-Yuan and Zhang, Xiaohan and Wu, Zhe and Yu, Beinan and Wang, Fang and Bai, Jie and Shen, Hui-Liang , journal=

  22. [30]

    Bai, Xiaokai and Zheng, Lianqing and Guan, Runwei and Cao, Si-Yuan and Shen, Hui-Liang , journal=

  23. [31]

    AI Perspectives , volume=

    Scheiner, Nicolas and Kraus, Florian and Appenrodt, Nils and Dickmann, J. AI Perspectives , volume=. 2021 , publisher=

    2021

  24. [32]

    2023 , publisher=

    Zheng, Lianqing and Li, Sen and Tan, Bin and Yang, Long and Chen, Sihan and Huang, Libo and Bai, Jie and Zhu, Xichan and Ma, Zhixiong , journal=. 2023 , publisher=

    2023

  25. [33]

    Wei, Yi and Zhao, Linqing and Zheng, Wenzhao and Zhu, Zheng and Zhou, Jie and Lu, Jiwen , journal=

  26. [34]

    Liao, Zhimin and Wei, Ping and Chen, Shuaijia and Wang, Haoxuan and Ren, Ziyang , booktitle=

  27. [35]

    Lang, Alex H and Vora, Sourabh and Caesar, Holger and Zhou, Lubing and Yang, Jiong and Beijbom, Oscar , booktitle=

  28. [36]

    Zhou, Yin and Tuzel, Oncel , booktitle=

  29. [37]

    Li, Hongyang and Zhang, Hao and Zeng, Zhaoyang and Liu, Shilong and Li, Feng and Ren, Tianhe and Zhang, Lei , booktitle=

  30. [38]

    Caesar, Holger and Bankiti, Varun and Lang, Alex H and Vora, Sourabh and Liong, Venice Erin and Xu, Qiang and Krishnan, Anush and Pan, Yu and Baldan, Giancarlo and Beijbom, Oscar , booktitle=

  31. [39]

    Zheng, Lianqing and Yang, Long and Lin, Qunshu and Ai, Wenjin and Liu, Minghao and Lu, Shouyi and Liu, Jianan and Ren, Hongze and Mo, Jingyue and Bai, Xiaokai and others , journal=

  32. [40]

    Fent, Felix and Kuttenreich, Fabian and Ruch, Florian and Rizwin, Farid and Juergens, Stefan and Lechermann, Lukas and Nissler, Christian and Perl, Andreas and Voll, Ulrich and Yan, Meng and others , journal=

  33. [41]

    Xiao, Yichun and Guan, Runwei and Ding, Fangqiang , journal=

  34. [42]

    Vora, Sourabh and Lang, Alex H and Helou, Bassam and Beijbom, Oscar , booktitle=

  35. [43]

    Li, Yiheng and Li, Hongyang and Huang, Zehao and Chang, Hong and Wang, Naiyan , journal=

  36. [44]

    Shi, Shaoshuai and Wang, Xiaogang and Li, Hongsheng , booktitle=

  37. [45]

    arXiv preprint 2405.13675 , archivePrefix=

    Zhu Yu and Runming Zhang and Jiacheng Ying and Junchen Yu and Xiaohai Hu and Lun Luo and Siyuan Cao and Huiliang Shen , year=. arXiv preprint 2405.13675 , archivePrefix=

    arXiv

  38. [46]

    Lin, Juan-Ting and Dai, Dengxin and Van Gool, Luc , booktitle=

  39. [47]

    Hassani, Ali and Walton, Steven and Li, Jiachen and Li, Shen and Shi, Humphrey , booktitle=

  40. [48]

    Yuxin Wu and Alexander Kirillov and Francisco Massa and Wan-Yen Lo and Ross Girshick , title =

  41. [49]

    Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng , journal=

  42. [50]

    2022 , organization=

    Wang, Li and Zhang, Xinyu and Xv, Baowei and Zhang, Jinzhao and Fu, Rong and Wang, Xiaoyu and Zhu, Lei and Ren, Haibing and Lu, Pingping and Li, Jun and others , booktitle=. 2022 , organization=

    2022

  43. [51]

    Xu, Baowei and Zhang, Xinyu and Wang, Li and Hu, Xiaomei and Li, Zhiwei and Pan, Shuyue and Li, Jun and Deng, Yongqiang , booktitle=

  44. [52]

    Harley, Adam W and Fang, Zhaoyuan and Li, Jie and Ambrus, Rares and Fragkiadaki, Katerina , booktitle=

  45. [53]

    Liu, Zhijian and Tang, Haotian and Amini, Alexander and Yang, Xinyu and Mao, Huizi and Rus, Daniela L and Han, Song , booktitle=

  46. [54]

    Li, Yinhao and Ge, Zheng and Yu, Guanyi and Yang, Jinrong and Wang, Zengran and Shi, Yukang and Sun, Jianjian and Li, Zeming , booktitle=

  47. [55]

    Li, Zhiqi and Yu, Zhiding and Wang, Wenhai and Anandkumar, Anima and Lu, Tong and Alvarez, Jose M , booktitle=

  48. [56]

    2024 , volume=

    Xiong, Weiyi and Liu, Jianan and Huang, Tao and Han, Qing-Long and Xia, Yuxuan and Zhu, Bing , journal=. 2024 , volume=

    2024

  49. [57]

    Gu, Zijian and Ma, Jianwei and Huang, Yan and Wei, Honghao and Chen, Zhanye and Zhang, Hui and Hong, Wei , booktitle=

  50. [58]

    Huang, Xun and Xu, Ziyu and Wu, Hai and Wang, Jinlong and Xia, Qiming and Xia, Yan and Li, Jonathan and Gao, Kyle and Wen, Chenglu and Wang, Cheng , booktitle=

  51. [59]

    Xu, Ruoyu and Xiang, Zhiyu and Zhang, Chenwei and Zhong, Hanzhi and Zhao, Xijun and Dang, Ruina and Xu, Peng and Pu, Tianyu and Liu, Eryun , booktitle=

  52. [60]

    Huang, Xun and Wang, Jinlong and Xia, Qiming and Chen, Siheng and Yang, Bisheng and Li, Xin and Wang, Cheng and Wen, Chenglu , booktitle=

  53. [61]

    Kim, Youngseok and Shin, Juyeb and Kim, Sanmin and Lee, In-Jae and Choi, Jun Won and Kum, Dongsuk , booktitle=

  54. [62]

    Rukhovich , booktitle=

  55. [63]

    Yin, Tianwei and Zhou, Xingyi and Krahenbuhl, Philipp , booktitle=

  56. [64]

    Li, Jinyu and Luo, Chenxu and Yang, Xiaodong , booktitle=

  57. [65]

    2020 , publisher=

    Shi, Shaoshuai and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng , journal=. 2020 , publisher=

    2020

  58. [66]

    2025 , organization=

    Song, Ziying and Yang, Lei and Xu, Shaoqing and Liu, Lin and Xu, Dongyang and Jia, Caiyan and Jia, Feiyang and Wang, Li , booktitle=. 2025 , organization=

    2025

  59. [67]

    2023 , publisher=

    Xie, Tao and Wang, Li and Wang, Ke and Li, Ruifeng and Zhang, Xinyu and Zhang, Haoming and Yang, Linqi and Liu, Huaping and Li, Jun , journal=. 2023 , publisher=

    2023

  60. [68]

    Yin, Junbo and Shen, Jianbing and Chen, Runnan and Li, Wei and Yang, Ruigang and Frossard, Pascal and Wang, Wenguan , booktitle=

  61. [69]

    2024 , publisher=

    Zhang, Haoyi and Wu, Kai and Chen, Rongkang and Wu, Zihao and Zhong, Yong and Li, Weihua , journal=. 2024 , publisher=

    2024

  62. [70]

    Qi, Charles R and Liu, Wei and Wu, Chenxia and Su, Hao and Guibas, Leonidas J , booktitle=

  63. [71]

    Zhang, Renrui and Qiu, Han and Wang, Tai and Guo, Ziyu and Cui, Ziteng and Qiao, Yu and Li, Hongsheng and Gao, Peng , booktitle=

  64. [72]

    Nabati, Ramin and Qi, Hairong , booktitle=

  65. [73]

    Kim, Youngseok and Kim, Sanmin and Choi, Jun Won and Kum, Dongsuk , booktitle=

  66. [74]

    Chen, Xuanyao and Zhang, Tianyuan and Wang, Yue and Wang, Yilun and Zhao, Hang , booktitle=

  67. [75]

    Yu, Zhu and Sheng, Zehua and Zhou, Zili and Luo, Lun and Cao, Si-Yuan and Gu, Hong and Zhang, Huaqi and Shen, Hui-Liang , booktitle=

  68. [76]

    Li, Zhiqi and Wang, Wenhai and Li, Hongyang and Xie, Enze and Sima, Chonghao and Lu, Tong and Qiao, Yu and Dai, Jifeng , booktitle=

  69. [77]

    Roddick, Thomas and Kendall, Alex and Cipolla, Roberto , journal=

  70. [78]

    Philion , booktitle=

  71. [79]

    2022 , publisher=

    Palffy, Andras and Pool, Ewoud and Baratam, Srimannarayana and Kooij, Julian FP and Gavrila, Dariu M , journal=. 2022 , publisher=

    2022

  72. [80]

    Zheng, Lianqing and Ma, Zhixiong and Zhu, Xichan and Tan, Bin and Li, Sen and Long, Kai and Sun, Weiqi and Chen, Sihan and Zhang, Lu and Wan, Mengyue and others , booktitle=

  73. [81]

    Sindagi, Vishwanath A and Zhou, Yin and Tuzel, Oncel , booktitle=

  74. [82]

    2024 , volume=

    Liu, Jianan and Zhao, Qiuchi and Xiong, Weiyi and Huang, Tao and Han, Qing-Long and Zhu, Bing , journal=. 2024 , volume=

    2024

  75. [83]

    Major, Bence and Fontijne, Daniel and Ansari, Amin and Teja Sukhavasi, Ravi and Gowaikar, Radhika and Hamilton, Michael and Lee, Sean and Grzechnik, Slawomir and Subramanian, Sundar , booktitle=

  76. [84]

    2021 , publisher=

    Wang, Yizhou and Jiang, Zhongyu and Li, Yudong and Hwang, Jenq-Neng and Xing, Guanbin and Liu, Hui , journal=. 2021 , publisher=

    2021

  77. [85]

    2022 , organization=

    Wang, Tai and Xinge, ZHU and Pang, Jiangmiao and Lin, Dahua , booktitle=. 2022 , organization=

    2022

  78. [86]

    2022 , organization=

    Liu, Yingfei and Wang, Tiancai and Zhang, Xiangyu and Sun, Jian , booktitle=. 2022 , organization=

    2022

  79. [87]

    2022 , organization=

    Wang, Yue and Guizilini, Vitor Campagnolo and Zhang, Tianyuan and Wang, Yilun and Zhao, Hang and Solomon, Justin , booktitle=. 2022 , organization=

    2022

  80. [88]

    Park, Dennis and Ambrus, Rares and Guizilini, Vitor and Li, Jie and Gaidon, Adrien , booktitle=

  81. [89]

    Wang, Tai and Zhu, Xinge and Pang, Jiangmiao and Lin, Dahua , booktitle=

  82. [90]

    2023 , publisher=

    Yao, Shanliang and Guan, Runwei and Huang, Xiaoyu and Li, Zhuoxiao and Sha, Xiangyu and Yue, Yong and Lim, Eng Gee and Seo, Hyungjoon and Man, Ka Lok and Zhu, Xiaohui and others , journal=. 2023 , publisher=

    2023

  83. [91]

    Lin, Zhiwei and Liu, Zhe and Xia, Zhongyu and Wang, Xinhao and Wang, Yongtao and Qi, Shengxiang and Dong, Yang and Dong, Nan and Zhang, Le and Zhu, Ce , booktitle=

  84. [92]

    2018 , publisher=

    Yan, Yan and Mao, Yuxing and Li, Bo , journal=. 2018 , publisher=

    2018

  85. [93]

    Wang, Haiyang and Tang, Hao and Shi, Shaoshuai and Li, Aoxue and Li, Zhenguo and Schiele, Bernt and Wang, Liwei , booktitle=

  86. [94]

    2023 , publisher=

    Mao, Jiageng and Shi, Shaoshuai and Wang, Xiaogang and Li, Hongsheng , journal=. 2023 , publisher=

    2023

  87. [95]

    Huang, Junjie and Huang, Guan and Zhu, Zheng and Ye, Yun and Du, Dalong , journal=

  88. [96]

    Park, Jinhyung and Xu, Chenfeng and Yang, Shijia and Keutzer, Kurt and Kitani, Kris and Tomizuka, Masayoshi and Zhan, Wei , journal=

  89. [97]

    He, Yong and Yu, Hongshan and Liu, Xiaoyan and Yang, Zhengeng and Sun, Wei and Anwar, Saeed and Mian, Ajmal , journal=

  90. [98]

    2020 , publisher=

    Guo, Yulan and Wang, Hanyun and Hu, Qingyong and Liu, Hao and Liu, Li and Bennamoun, Mohammed , journal=. 2020 , publisher=

    2020

  91. [99]

    2024 , publisher=

    Ma, Yuexin and Wang, Tai and Bai, Xuyang and Yang, Huitong and Hou, Yuenan and Wang, Yaming and Qiao, Yu and Yang, Ruigang and Zhu, Xinge , journal=. 2024 , publisher=

    2024

  92. [100]

    2022 , publisher=

    Roldao, Luis and De Charette, Raoul and Verroust-Blondet, Anne , journal=. 2022 , publisher=

    2022

  93. [101]

    Cao, Anh-Quan and De Charette, Raoul , booktitle=

  94. [102]

    IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion , author=. IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  95. [103]

    Weng, Xinshuo and Wang, Jianren and Held, David and Kitani, Kris , booktitle=

  96. [104]

    2022 , organization=

    Pang, Ziqi and Li, Zhichao and Wang, Naiyan , booktitle=. 2022 , organization=

    2022

  97. [105]

    2024 , organization=

    Pan, Zhijun and Ding, Fangqiang and Zhong, Hantao and Lu, Chris Xiaoxuan , booktitle=. 2024 , organization=

    2024

  98. [106]

    2016 , organization=

    Milletari, Fausto and Navab, Nassir and Ahmadi, Seyed-Ahmad , booktitle=. 2016 , organization=

    2016

  99. [107]

    2024 , publisher=

    Zhang, Xiaohan and Zhang, Xue and Cao, Si-Yuan and Yu, Beinan and Zhang, Chenghao and Shen, Hui-Liang , journal=. 2024 , publisher=

    2024

  100. [108]

    2015 , organization=

    Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas , booktitle=. 2015 , organization=

    2015

  101. [109]

    Cai, Zhaowei and Vasconcelos, Nuno , booktitle=

  102. [110]

    Jiang, Xiaohui and Li, Shuailin and Liu, Yingfei and Wang, Shihao and Jia, Fan and Wang, Tiancai and Han, Lijin and Zhang, Xiangyu , booktitle=

  103. [111]

    Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J , booktitle=

  104. [112]

    Yang, Zetong and Sun, Yanan and Liu, Shu and Jia, Jiaya , booktitle=

  105. [113]

    Chen, Yukang and Liu, Jianhui and Zhang, Xiangyu and Qi, Xiaojuan and Jia, Jiaya , booktitle=

  106. [114]

    Palmer, Patrick and Krueger, Martin and Altendorfer, Richard and Adam, Ganesh and Bertram, Torsten , journal=

  107. [115]

    Advances in Neural Information Processing Systems , volume=

    Yin, Tianwei and Zhou, Xingyi and Kr. Advances in Neural Information Processing Systems , volume=

  108. [116]

    Huang, Tengteng and Liu, Zhe and Chen, Xiwu and Bai, Xiang , booktitle=

  109. [117]

    Yang, Zeyu and Chen, Jiaqi and Miao, Zhenwei and Li, Wei and Zhu, Xiatian and Zhang, Li , journal=

  110. [118]

    He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian , booktitle=

  111. [119]

    IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Lin, Tsung-Yi and Doll. IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  112. [120]

    IEEE/CVF International Conference on Computer Vision , pages=

    Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll. IEEE/CVF International Conference on Computer Vision , pages=

  113. [121]

    Loshchilov, Ilya and Hutter, Frank , journal=

  114. [122]

    European Conference on Computer Vision , pages=

    Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll. European Conference on Computer Vision , pages=

  115. [123]

    2023 , publisher=

    Liu, Zhanwen and Cheng, Juanru and Fan, Jin and Lin, Shan and Wang, Yang and Zhao, Xiangmo , journal=. 2023 , publisher=

    2023

  116. [124]

    2023 , publisher=

    Zhang, Yuxiao and Carballo, Alexander and Yang, Hanting and Takeda, Kazuya , journal=. 2023 , publisher=

    2023

  117. [125]

    Xu, Chenfeng and Wu, Bichen and Hou, Ji and Tsai, Sam and Li, Ruilong and Wang, Jialiang and Zhan, Wei and He, Zijian and Vajda, Peter and Keutzer, Kurt and others , booktitle=

  118. [126]

    Tian, Fengrui and Du, Shaoyi and Duan, Yueqi , booktitle=

  119. [127]

    2020 , publisher=

    Wu, Yutian and Wang, Yueyu and Zhang, Shuwei and Ogai, Harutoshi , journal=. 2020 , publisher=

    2020

  120. [128]

    Long, Yunfei and Kumar, Abhinav and Morris, Daniel and Liu, Xiaoming and Castro, Marcos and Chakravarty, Punarjay , booktitle=

  121. [129]

    arXiv preprint arXiv:2403.15313 , year=

    Baumann, Nicolas and Baumgartner, Michael and Ghignone, Edoardo and K. arXiv preprint arXiv:2403.15313 , year=

    arXiv

  122. [130]

    2024 , organization=

    Kim, Jisong and Seong, Minjae and Bang, Geonho and Kum, Dongsuk and Choi, Jun Won , booktitle=. 2024 , organization=

    2024

  123. [131]

    2019 , organization=

    Meyer, Michael and Kuschk, Georg , booktitle=. 2019 , organization=

    2019

  124. [132]

    Paek, Dong-Hee and Kong, Seung-Hyun and Wijaya, Kevin Tirta , journal=

  125. [133]

    MMDetection3D Contributors , howpublished =

  126. [134]

    Geiger, Andreas and Lenz, Philip and Urtasun, Raquel , booktitle=

  127. [135]

    Sensors , volume=

    Zhou, Yi and Liu, Lulu and Zhao, Haocheng and L. Sensors , volume=. 2022 , publisher=

    2022

  128. [136]

    IEEE/CVF International Conference on Computer Vision , pages=

    Wu, Zongwei and Paudel, Danda Pani and Fan, Deng-Ping and Wang, Jingjing and Wang, Shuo and Demonceaux, C. IEEE/CVF International Conference on Computer Vision , pages=

  129. [137]

    Zhu, Xinge and Zhou, Hui and Wang, Tai and Hong, Fangzhou and Ma, Yuexin and Li, Wei and Li, Hongsheng and Lin, Dahua , booktitle=

  130. [138]

    Lu, Fan and Chen, Guang and Liu, Yinlong and Zhang, Lijun and Qu, Sanqing and Liu, Shu and Gu, Rongqi , booktitle=

  131. [139]

    2023 , publisher=

    Liu, Hao and Ma, Yanni and Hu, Qingyong and Guo, Yulan , journal=. 2023 , publisher=

    2023

  132. [140]

    2023 , publisher=

    Li, Jie and Song, Qi and Yan, Xiaohu and Chen, Yongquan and Huang, Rui , journal=. 2023 , publisher=

    2023

  133. [141]

    2022 , publisher=

    Shan, Jiayao and Zhou, Sifan and Cui, Yubo and Fang, Zheng , journal=. 2022 , publisher=

    2022

  134. [142]

    2023 , publisher=

    Wang, Jian and Li, Fan and Zhang, Xuchong and Sun, Hongbin , journal=. 2023 , publisher=

    2023

  135. [143]

    2024 , publisher=

    Guo, Shuai and Shi, Lei and Jiang, Xiaoheng and Lv, Pei and Liu, Qidong and Hu, Yazhou and Ji, Rongrong and Xu, Mingliang , journal=. 2024 , publisher=

    2024

  136. [144]

    2023 , publisher=

    Wang, Shuhua and Lu, Ke and Xue, Jian and Zhao, Yang , journal=. 2023 , publisher=

    2023

  137. [145]

    2024 , publisher=

    Zeng, Shuai and Zheng, Wenzhao and Lu, Jiwen and Yan, Haibin , journal=. 2024 , publisher=

    2024

  138. [146]

    2023 , publisher=

    An, Pei and Duan, Yucong and Huang, Yuliang and Ma, Jie and Chen, Yanfei and Wang, Liheng and Yang, You and Liu, Qiong , journal=. 2023 , publisher=

    2023

  139. [147]

    2024 , publisher=

    Wang, Lin and Sun, Shiliang and Zhao, Jing , journal=. 2024 , publisher=

    2024

  140. [148]

    2023 , organization=

    Wu, Zizhang and Chen, Guilian and Gan, Yuanzhu and Wang, Lei and Pu, Jian , booktitle=. 2023 , organization=

    2023

  141. [149]

    Ye, Tengju and Jing, Wei and Hu, Chunyong and Huang, Shikun and Gao, Lingping and Li, Fangzhen and Wang, Jingke and Guo, Ke and Xiao, Wencong and Mao, Weibo and others , journal=

  142. [150]

    2025 , publisher=

    Xiong, Weiyi and Zou, Zean and Zhao, Qiuchi and He, Fengchun and Zhu, Bing , journal=. 2025 , publisher=

    2025

  143. [151]

    2024 , organization=

    Zhao, Cheng and Sun, Su and Wang, Ruoyu and Guo, Yuliang and Wan, Jun-Jun and Huang, Zhou and Huang, Xinyu and Chen, Yingjie Victor and Ren, Liu , booktitle=. 2024 , organization=

    2024

  144. [152]

    Kung, Pou-Chun and Harisha, Skanda and Vasudevan, Ram and Eid, Aline and Skinner, Katherine A , journal=

  145. [153]

    Khan, Mustafa and Fazlali, Hamidreza and Sharma, Dhruv and Cao, Tongtong and Bai, Dongfeng and Ren, Yuan and Liu, Bingbing , journal=

  146. [154]

    Sun, Su and Zhao, Cheng and Sun, Zhuoyang and Chen, Yingjie Victor and Chen, Mei , booktitle=

  147. [155]

    Zhou, Xiaoyu and Lin, Zhiwei and Shan, Xiaojun and Wang, Yongtao and Sun, Deqing and Yang, Ming-Hsuan , booktitle=

  148. [156]

    2024 , organization=

    Huang, Yuanhui and Zheng, Wenzhao and Zhang, Yunpeng and Zhou, Jie and Lu, Jiwen , booktitle=. 2024 , organization=

    2024

  149. [157]

    Tian, Qijian and Tan, Xin and Xie, Yuan and Ma, Lizhuang , booktitle=

  150. [158]

    IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Hess, Georg and Lindstr. IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  151. [159]

    Miao, Sheng and Huang, Jiaxin and Bai, Dongfeng and Yan, Xu and Zhou, Hongyu and Wang, Yue and Liu, Bingbing and Geiger, Andreas and Liao, Yiyi , booktitle=

  152. [160]

    Yang, Ziyi and Gao, Xinyu and Zhou, Wen and Jiao, Shaohui and Zhang, Yuqing and Jin, Xiaogang , booktitle=

  153. [161]

    ACM Transactions on Graphics , volume=

    Kerbl, Bernhard and Kopanas, Georgios and Leimk. ACM Transactions on Graphics , volume=

  154. [162]

    Liu, Haisong and Teng, Yao and Lu, Tao and Wang, Haiguang and Wang, Limin , booktitle=

  155. [163]

    2022 , publisher=

    Zhu, Hanqi and Deng, Jiajun and Zhang, Yu and Ji, Jianmin and Mao, Qiuyu and Li, Houqiang and Zhang, Yanyong , journal=. 2022 , publisher=

    2022

  156. [164]

    Klingner, Marvin and Borse, Shubhankar and Kumar, Varun Ravi and Rezaei, Behnaz and Narayanan, Venkatraman and Yogamani, Senthil and Porikli, Fatih , booktitle=

  157. [165]

    Wang, Shihao and Liu, Yingfei and Wang, Tiancai and Li, Ying and Zhang, Xiangyu , booktitle=

  158. [166]

    Huang, Junjie and Huang, Guan , journal=

  159. [167]

    Wang, Yuqi and Chen, Yuntao and Liao, Xingyu and Fan, Lue and Zhang, Zhaoxiang , booktitle=

  160. [168]

    Wang, Xiaofeng and Zhu, Zheng and Xu, Wenbo and Zhang, Yunpeng and Wei, Yi and Chi, Xu and Ye, Yun and Du, Dalong and Lu, Jiwen and Wang, Xingang , booktitle=

  161. [169]

    Zheng, Lianqing and Liu, Jianan and Guan, Runwei and Yang, Long and Lu, Shouyi and Li, Yuanzhe and Bai, Xiaokai and Bai, Jie and Ma, Zhixiong and Shen, Hui-Liang and Zhu, Xichan , journal=

  162. [170]

    Zhou, Qiu and Cao, Jinming and Leng, Hanchao and Yin, Yifang and Kun, Yu and Zimmermann, Roger , booktitle=

  163. [171]

    Yang, Zhen and Dong, Yanpeng and Wang, Jiayu and Wang, Heng and Ma, Lichao and Cui, Zijian and Liu, Qi and Pei, Haoran and Zhang, Kexin and Zhang, Chao , journal=

  164. [172]

    Hong, Yu and Liu, Qian and Cheng, Huayuan and Ma, Danjiao and Dai, Hang and Wang, Yu and Cao, Guangzhi and Ding, Yong , journal=

  165. [173]

    Tian, Xiaoyu and Jiang, Tao and Yun, Longfei and Mao, Yucheng and Yang, Huitong and Wang, Yue and Wang, Yilun and Zhao, Hang , journal=

  166. [174]

    Ming, Zhenxing and Berrio, Julie Stephany and Shan, Mao and Worrall, Stewart , journal=

  167. [175]

    Chen, Xuesong and Shi, Shaoshuai and Ma, Tao and Zhou, Jingqiu and See, Simon and Cheung, Ka Chun and Li, Hongsheng , booktitle=

  168. [176]

    Kang, Changwon and Kim, Jisong and Shin, Hongjae and Park, Junseo and Choi, Jun Won , booktitle=

Pith tools

Reviewed July 14, 2026 · model on record in the stance chip above.