Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read HAMMER shows a single server can fuse asynchronous heterogeneous robot streams into one live metric-semantic 3D Gaussian map, without any prior relative poses.

desk verdict A well-engineered multi-robot 3DGS system whose core alignment statistics are under-reported; the evaluation gaps are real but fixable, and the paper deserves review. read the letter →

arxiv 2501.14147 v2 pith:HFQVTQWB submitted 2025-01-24 cs.RO

Javier Yu , Timothy Chen , Mac Schwager This is my paper · ORCID
classification cs.RO MSC 68T4068T45
keywords multi-robotmapping3DGaussiansplattingmetric-semanticframealignmentonlineheterogeneousrobotsCLIPembeddingslanguage-guidednavigation
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

HAMMER proposes a server-based pipeline in which a team of robots and wearable devices, each running its own onboard SLAM in its own local coordinate frame, streams posed color and depth data over ROS to a single GPU server. The server performs a one-time alignment of each new device's frame to a global frame using image correspondences and localized structure-from-motion, with no prior knowledge of relative poses. It then continuously trains a metric-semantic 3D Gaussian Splatting map from the fused streams, distilling CLIP embeddings into a feature field that supports open-vocabulary queries. The paper reports that this yields higher-fidelity maps than competing multi-robot mapping baselines in simulation, and better novel-view reconstruction than a Di-NeRF-based baseline in hardware trials with Aria glasses and ground robots, at a fraction of the compute time.

What carries the argument

The load-bearing component is the one-time inter-robot frame alignment module. It builds on a rotation-aware absolute orientation problem, Eq. (1), which for a window of $W=16$ poses per robot jointly optimizes scale, rotation, and translation between local SLAM poses and localized SfM poses with a small rotation regularization term; composing two such solutions maps the new device's frame $T_j$ into the global frame $T_g$. Because the SfM step ignores onboard poses entirely, the method treats each robot's localization as a black box and only requires one robot to provide metric-scale poses.

What would settle it

Deploy two robots in a scene with deliberately disjoint coverage so the only overlap is a single narrow doorway, or inject growing drift into one robot's SLAM poses; if the one-time alignment is rejected or the final map's PSNR drops sharply compared to an Oracle bulk-SfM map, the claimed independence from coverage and drift would be falsified.

Watch

Extended reading notes

Core claim

The paper's central claim is that heterogeneous multi-robot mapping can be decoupled into a cheap one-time per-robot alignment step plus an online, continually trained 3D Gaussian Splatting map, so that no robot needs a GPU and no prior inter-robot pose knowledge is required. HAMMER finds candidate correspondences between a new device and already-aligned devices with NetVLAD and SuperPoint/LightGlue, runs localized SfM on a 32-image window to get an independent relative pose estimate, and solves a rotation-aware absolute orientation problem (its Eq. (1)) to compose a metric similarity transform from the new robot's local frame into the global frame. After alignment, all streams are fused into a 3DGS representation with online pose refinement, bilateral-grid ISP compensation to handle differing camera pipelines, and a hash-grid feature field supervised by CLIP embeddings for language semantics. Against CP-SLAM and MAGiC-SLAM on ReplicaMultiAgent it reports higher average PSNR/SSIM and lower LPIPS at 8 minutes versus 133+ minutes, and in real-world trials it outperforms a centralized Di-NeRF* baseline while approaching an Oracle bulk-SfM upper bound.

Load-bearing premise

HAMMER's central assumption is that each new device's path has enough visual overlap with already-aligned devices that a 32-image window yields a correct similarity transform, and that onboard SLAM drift stays small enough for the online pose refinement to correct; a robot that explores brand-new territory or drifts heavily can fail to align or can bias the global map.

Editorial extensions

If this is right

  • Any device that can stream posed RGB-D data or point clouds can join an existing map after a single roughly 36-second alignment, regardless of its camera or SLAM software.
  • A single server GPU can sustain 26-66 Hz training updates over the fused stream, making the map usable during deployment rather than after offline batch optimization.
  • Semantic queries such as "go to the couch" run in about 20 ms by matching CLIP embeddings against the feature field, and Splat-Nav plans collision-free trajectories in about 0.5 s for the whole team.
  • On ReplicaMultiAgent, the reported reconstruction quality is higher than CP-SLAM and MAGiC-SLAM while using under a tenth of the computation time.

Reading between the lines

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

  • Beyond the paper, if the 32-image localized SfM window is the bottleneck, then scenes with low texture or small overlaps will need larger windows or periodic re-alignment; this scaling condition is not explored in the paper.
  • A further implication is that because only the origin robot must be metric, teams mixing AR glasses, drones, and ground robots can all contribute to one map, which could enable wearable-plus-robot search-and-rescue or home assistance without dedicated infrastructure.
  • One testable extension is whether the same ISP compensation that handles device-to-device appearance differences also handles large lighting changes over time within a single device, since the paper's scenes include such variation but do not isolate it.
  • The paper names RGB-only devices as future work; an immediate test would be replacing depth input with monocular metric depth estimation and comparing alignment and map quality against the RGB-D results.
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 / 6 minor

Summary. HAMMER proposes a server-based, online multi-robot 3D Gaussian Splatting mapping system that fuses asynchronous, heterogeneous robot and edge-device data streams (e.g., ground robots and Aria glasses) without prior knowledge of relative poses. The method has two main components: (i) a one-time inter-robot frame alignment module that uses NETVLAD/SuperPoint/LightGlue correspondences, localized SfM on a 32-image window, and two absolute-orientation solves to estimate a similarity transform from each robot's local SLAM frame to the global map frame; and (ii) an online semantic 3DGS training module that continuously optimizes a map from the aligned streams, with appearance compensation for different ISP pipelines, depth supervision, and CLIP semantic embedding distillation for open-vocabulary queries. The paper reports evaluations on the ReplicaMultiAgent dataset against CP-SLAM and MAGiC-SLAM, and on two real-world hardware scenes against several baselines, including a modified Di-NeRF baseline and an Oracle upper bound. The authors claim HAMMER is the first online multi-robot 3DGS pipeline for heterogeneous teams and that it produces higher-fidelity maps than competing baselines while requiring far less computation.

Significance. If the central claim is supported, HAMMER would be a practically valuable contribution: it offers a single-GPU server architecture that works with off-the-shelf robot pose estimators and ROS infrastructure, which is a meaningful step toward deployable collaborative semantic mapping. The paper's strengths include a well-motivated system design, a genuinely one-time and black-box alignment procedure, real hardware experiments with heterogeneous devices, and an explicit semantic field that enables language-guided downstream tasks. The authors also provide useful limitation statements, notably that devices without pose estimates, significant SLAM drift, and very short deployments are not handled. However, the quantitative evidence for the headline claims has important gaps: the central alignment module is not evaluated with any success/failure statistics, the main benchmark table reports training-view rather than held-out novel-view quality, and the real-world baselines are substantially modified or partially self-referential. These gaps are load-bearing, because the paper's principal contributions are the alignment method and the claimed fidelity advantage over existing baselines.

major comments (4)
  1. [III-B.3, III-B.4, Sec. V] The one-time alignment module is the load-bearing component for the 'no prior relative pose' claim, yet the paper reports no statistics on how often alignment succeeded, how many SfM attempts were made, how many were rejected by the 0.1 m / 10 deg thresholds, or what the residuals of the accepted Eq. (1) solves were. Without this information, the hardware results in Sec. IV-B.2 are conditional on an unmeasured alignment success rate, and the conclusion's own admission that drift and very short deployments 'can break the method' highlights the regime in which the alignment must work. I ask the authors to report alignment outcome distributions, residuals, and a sensitivity analysis over the window size W, the match-ratio xi, and the rejection thresholds, including at least one case where alignment fails or is marginal.
  2. [Table I, Sec. IV-B.1] The caption of Table I states that the reported numbers are 'training view synthesis' metrics, not held-out novel-view synthesis. Training-view PSNR/SSIM/LPIPS can be inflated by overfitting, so the claim that HAMMER 'outperforms both baselines' on reconstruction quality is not established by this table. Additionally, the CP-SLAM metrics are copied from [13] and the runtimes are estimated rather than measured under the same protocol, making the comparison uncontrolled. Please either evaluate all methods on a common held-out view set or clearly state that Table I is an approximate cross-paper comparison rather than a controlled benchmark.
  3. [IV-A.3, Fig. 5] The Di-NeRF* baseline is so heavily modified (centralized optimization, training on cached HAMMER data, and replacement of the NeRF with 3DGS) that the comparison does not evaluate the actual Di-NeRF method; it evaluates a custom centralized 3DGS variant with gradient-based alignment. This weakens the claim that HAMMER 'dramatically outperforms Di-NeRF*' as a statement about the published baseline. Relatedly, the Oracle baseline is trained on the full dataset with SfM poses derived from the same sensor data, so it is not an independent ground truth; it is an upper bound only relative to the SfM pose pipeline. The authors should clarify what each baseline is intended to show and, if possible, include an unmodified Di-NeRF or a centralized 3DGS without HAMMER's pose refinement to isolate the contributions.
  4. [Sec. IV-B.4, Sec. III-C.3] The semantic contribution is evaluated only through a single qualitative case study, with no quantitative measure of language-guided goal localization accuracy, and the contribution of the online pose refinement in Sec. III-C.3 is not ablated. Since the abstract and introduction emphasize both the semantic capability and the continual online training, I would expect at least a small ablation (e.g., with and without pose refinement, with and without ISP compensation) and a quantitative semantic localization metric (e.g., success rate or distance error for language-specified goals).
minor comments (6)
  1. [Abstract, Introduction] The abstract claims '2x' higher-fidelity maps while the introduction says 'more than 40% better Mean-Squared-Error'; these are inconsistent and the '2x' is not defined anywhere in the paper. Please state the metric and the exact comparison.
  2. [III-B.4] The value of the rotation regularization weight epsilon in Eq. (1) is never specified, and the statement 'for small epsilon, an approximately optimal solution can be computed' is vague. Please report the chosen value and the number of iterations or the stopping criterion for the alternating optimization.
  3. [III-C.2] There is a typo: 'streaming' appears as 'steaming' in the text, and the sampling-weighting scheme for data tuples is described only qualitatively. Please clarify how the sampling probability is computed.
  4. [IV-A.3] The paper states that neither public implementation of CP-SLAM or MAGiC-SLAM is real-time capable, but MAGiC-SLAM is described elsewhere as requiring N+1 GPUs; this should be clarified so that the runtime comparison in Table I is not misinterpreted as an unfair hardware handicap.
  5. [Fig. 5] The figure plots 'Map quality over time' but the axes and the exact metric (PSNR on held-out frames? per-device averages?) are not clearly labeled in the text. Please add explicit axis labels and describe how the curves were computed.
  6. [IV-B.2] The held-out evaluation set consists of 10 frames per device, but the paper does not report standard deviations or significance tests over these small samples. Given the small number of frames, confidence intervals would help assess whether the reported differences are meaningful.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: HAMMER's central fidelity and alignment claims are evaluated against external benchmarks and held-out views, and no load-bearing argument reduces to its own inputs.

full rationale

The paper makes no derivation-style claim: HAMMER is a pipeline whose contributions are a one-time alignment module and an online 3DGS training scheme. The main fidelity claim is checked against published baselines (CP-SLAM and MAGiC-SLAM on ReplicaMultiAgent; Di-NeRF* and Individuals in hardware trials) using held-out evaluation frames, so the higher-fidelity maps result is not a restatement of a fitted quantity. The one-time alignment in Sec. III-B solves Eq. (1) between independent localized SfM poses and on-board SLAM poses; no output of that optimization is presupposed as a target, and the no-prior-relative-pose property is an architectural feature rather than a derived result. The Oracle baseline is intentionally stronger by construction, not the source of HAMMER's measured performance. The rejection thresholds on Eq. (1) residuals are a quality filter whose per-attempt success rate is not reported; that is an empirical reporting limitation noted in the hardware trials section, not a circular reduction. The Conclusion and Limitations section openly states that drift and very short deployments can degrade the method, which further supports that the claims are conditional rather than tautological. Self-citations (LEGS, NerfBridge, Splat-Nav) are implementation or related-work references and are not load-bearing to the central claims.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim rests on several hand-set hyperparameters (W, xi, rejection thresholds, epsilon, initial opacity) and domain assumptions about metric pose availability, visual overlap, reliability of localized SfM, and convergence of online 3DGS training. No new physical entities are introduced.

free parameters (5)
  • W (SfM window size) = 16
    Number of images from each robot used in localized SfM; hand-chosen and central to alignment cost (Sec III-B3).
  • xi (feature match ratio) = 0.25
    Minimum fraction of SuperPoint/LightGlue matches to accept an image pair as a correspondence (Sec III-B1).
  • alignment rejection thresholds = 0.1 m, 10 deg
    Translation and rotation residuals from Eq. (1) used to accept or reject alignments; hand-set and not derived (Sec III-B4).
  • epsilon (rotation regularization weight) = not specified
    Small weight on the rotation term in Eq. (1) enabling an approximate closed-form solution; value not reported (Sec III-B4).
  • initial Gaussian opacity sigma = 0.3
    Opacity initialization for spawned Gaussians (Sec III-C2).
assumptions (6)
  • domain assumption At least one robot provides pose estimates in a metric coordinate frame.
    Sec III-A states this assumption; it is needed to scale all other frames to a metric global frame.
  • domain assumption Sufficient visual overlap exists among data streams so inter-robot alignment is possible.
    Sec III-A states this assumption; if no overlapping views exist, correspondences and SfM fail.
  • domain assumption Localized SfM with SuperPoint and SuperGlue produces a reliable relative pose estimate from a 32-image window.
    Sec III-B3 relies on this without verification against external ground truth.
  • domain assumption Online 3DGS optimization with pose refinement converges to a visually consistent map under streaming heterogeneous data.
    Sec III-C relies on differentiable rendering and photometric loss smoothness; no convergence guarantee is given.
  • standard math Umeyama absolute orientation and the approximate solution to Eq. (1) yield a valid similarity transform for small epsilon.
    Sec III-B4 invokes a standard result, but the rotation-aware regularized variant has no closed form and no optimality proof in the paper.
  • domain assumption CLIP embeddings transferred to 3D via a hash-grid feature field support open-vocabulary queries.
    Sec III-C1 relies on zero-shot CLIP generalization; only one qualitative navigation demo is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting." pith.science (2026). https://pith.science/paper/HFQVTQWB

@misc{pith2026250114147,
  author       = {Pith},
  title        = {Pith review of: HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HFQVTQWB}},
  note         = {Machine review of arXiv:2501.14147}
}
read the original abstract

3D Gaussian Splatting offers expressive scene reconstruction, modeling a broad range of visual, geometric, and semantic information. However, efficient real-time map reconstruction with data streamed from multiple robots and devices remains a challenge. To that end, we propose HAMMER, a server-based collaborative Gaussian Splatting method that leverages widely available ROS communication infrastructure to generate 3D, metric-semantic maps from asynchronous robot data-streams with no prior knowledge of initial robot positions and varying on-device pose estimators. HAMMER consists of (i) a frame alignment module that transforms local SLAM poses and image data into a global frame and requires no prior relative pose knowledge, and (ii) an online module for training semantic 3DGS maps from streaming data. HAMMER handles mixed perception modes, adjusts automatically for variations in image pre-processing among different devices, and distills CLIP semantic codes into the 3D scene for open-vocabulary language queries. In our real-world experiments, HAMMER creates higher-fidelity maps (2x) compared to competing baselines and is useful for downstream tasks, such as semantic goal-conditioned navigation (e.g., "go to the couch"). Accompanying content available at hammer-project.github.io.

Figures

Figures reproduced from arXiv: 2501.14147 by the authors.

Figure 1
Figure 1. HAMMER takes streaming image and depth data from a heterogeneous team of robots and edge devices (e.g. Aria [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. HAMMER uses a one-time computation to align a new robot’s data stream with the server’s map frame. We match images from the unaligned robot with images from the server map using a place recognition feature extractor. When a correspondence is verified, matched images from robot and server are passed to an SfM solver, from which we find a single transform to align the robot to the server map frame. From then on, image… view at source ↗
Figure 3
Figure 3. Meshes extracted from HAMMER’s map during runtime ( [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Rendered evaluation images RGB (left) and depth (right) of HAMMER and two baselines across devices. HAMMER [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Map quality over time for HAMMER and baselines in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Motion plans for robots navigating to language [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. VISTA: Open-Vocabulary, Task-Relevant Robot Exploration with Online Semantic Gaussian Splatting

    cs.RO 2025-07 conditional novelty 6.0 of 10

    VISTA couples a view-diversity information metric with CLIP semantics in a receding-horizon planner to improve open-vocabulary object search during online Gaussian Splatting mapping on robots.

Reference graph

Works this paper leans on

39 extracted references · 19 canonical work pages · cited by 1 Pith paper

  1. [13]

    Magic-slam: Multi-agent gaussian globally consistent slam,

    V . Yugay, T. Gevers, and M. R. Oswald, “Magic-slam: Multi-agent gaussian globally consistent slam,” arXiv preprint arXiv:2411.16785 , 2024

    arXiv 2024

  2. [1]

    Project aria: A new tool for egocentric multi-modal ai research,

    J. Engel, K. Somasundaram, M. Goesele, A. Sun, A. Gamino, A. Turner, A. Talattof, A. Yuan, B. Souti, B. Meredith et al. , “Project aria: A new tool for egocentric multi-modal ai research,” arXiv preprint arXiv:2308.13561, 2023. 8 Fig. 6: Motion plans for robots navigating to language- specified goals with trajectories from Splat-Nav [7]

    arXiv 2023

  3. [2]

    Nerf: Representing scenes as neural radiance fields for view synthesis,

    B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, 2021

    work page 2021

  4. [3]

    3d gaussian splatting for real-time radiance field rendering,

    B. Kerbl, G. Kopanas, T. Leimk ¨uhler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics, vol. 42, no. 4, pp. 1–14, 2023

    2023

  5. [4]

    Gaussian splatting slam,

    H. Matsuki, R. Murai, P. H. Kelly, and A. J. Davison, “Gaussian splatting slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 18 039–18 048

    2024

  6. [5]

    Safer-splat: A control barrier function for safe navigation with online gaussian splatting maps,

    T. Chen, A. Swann, J. Yu, O. Shorinwa, R. Murai, M. Kennedy III, and M. Schwager, “Safer-splat: A control barrier function for safe navigation with online gaussian splatting maps,” arXiv preprint arXiv:2409.09868, 2024

    arXiv 2024

  7. [6]

    Splat-mover: multi-stage, open- vocabulary robotic manipulation via editable gaussian splatting,

    O. Shorinwa, J. Tucker, A. Smith, A. Swann, T. Chen, R. Firoozi, M. D. Kennedy, and M. Schwager, “Splat-mover: multi-stage, open- vocabulary robotic manipulation via editable gaussian splatting,” in 8th Annual Conference on Robot Learning , 2024

    work page 2024

  8. [7]

    Splat-nav: Safe real-time robot navigation in gaussian splatting maps,

    T. Chen, O. Shorinwa, W. Zeng, J. Bruno, P. Dames, and M. Schwager, “Splat-nav: Safe real-time robot navigation in gaussian splatting maps,” arXiv preprint arXiv:2403.02751 , 2024

    arXiv 2024

Show all 39 references
  1. [8]

    Gauu-scene: A scene reconstruction benchmark on large scale 3d reconstruction dataset using gaussian splatting,

    B. Xiong, Z. Li, and Z. Li, “Gauu-scene: A scene reconstruction benchmark on large scale 3d reconstruction dataset using gaussian splatting,” arXiv preprint arXiv:2401.14032 , 2024

    2024 arXiv

  2. [9]

    Driv- inggaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes,

    X. Zhou, Z. Lin, X. Shan, Y . Wang, D. Sun, and M.-H. Yang, “Driv- inggaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 634–21 643

    2024

  3. [10]

    Language-embedded gaussian splats (legs): Incrementally building room-scale representations with a mobile robot,

    J. Yu, K. Hari, K. Srinivas, K. El-Refai, A. Rashid, C. M. Kim, J. Kerr, R. Cheng, M. Z. Irshad, A. Balakrishna et al. , “Language-embedded gaussian splats (legs): Incrementally building room-scale representations with a mobile robot,” arXiv preprint arXiv:2409.18108 , 2024

    2024 arXiv

  4. [11]

    Dinno: Distributed neural net- work optimization for multi-robot collaborative learning,

    J. Yu, J. A. Vincent, and M. Schwager, “Dinno: Distributed neural net- work optimization for multi-robot collaborative learning,”IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1896–1903, 2022

    1903

  5. [12]

    Distributed optimiza- tion methods for multi-robot systems: Part II–A survey,

    O. Shorinwa, T. Halsted, J. Yu, and M. Schwager, “Distributed optimiza- tion methods for multi-robot systems: Part II–A survey,” arXiv preprint arXiv:2301.11361, 2023

    2023 arXiv

  6. [14]

    Cp-slam: Collabora- tive neural point-based slam system,

    J. Hu, M. Mao, H. Bao, G. Zhang, and Z. Cui, “Cp-slam: Collabora- tive neural point-based slam system,” Advances in Neural Information Processing Systems, vol. 36, pp. 39 429–39 442, 2023

    2023

  7. [15]

    The replica dataset: A digital replica of indoor spaces,

    J. Straub, T. Whelan, L. Ma, Y . Chen, E. Wijmans, S. Green, J. J. Engel, R. Mur-Artal, C. Ren, S. Verma et al. , “The replica dataset: A digital replica of indoor spaces,” arXiv preprint arXiv:1906.05797 , 2019

    1906 arXiv

  8. [16]

    Di-nerf: Distributed nerf for collaborative learning with relative pose refinement,

    M. Asadi, K. Zareinia, and S. Saeedi, “Di-nerf: Distributed nerf for collaborative learning with relative pose refinement,” IEEE Robotics and Automation Letters, 2024

    2024

  9. [17]

    Instant neural graphics primitives with a multiresolution hash encoding,

    T. M ¨uller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Transactions on Graphics (ToG), vol. 41, no. 4, pp. 1–15, 2022

    2022

  10. [18]

    Structure-from-motion revisited,

    J. L. Sch ¨onberger and J.-M. Frahm, “Structure-from-motion revisited,” in Conference on Computer Vision and Pattern Recognition , 2016

    2016

  11. [19]

    Splatam: Splat track & map 3d gaussians for dense rgb-d slam,

    N. Keetha, J. Karhade, K. M. Jatavallabhula, G. Yang, S. Scherer, D. Ramanan, and J. Luiten, “Splatam: Splat track & map 3d gaussians for dense rgb-d slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 357–21 366

    2024

  12. [20]

    Disco-slam: Distributed scan context-enabled multi-robot lidar slam with two-stage global-local graph optimization,

    Y . Huang, T. Shan, F. Chen, and B. Englot, “Disco-slam: Distributed scan context-enabled multi-robot lidar slam with two-stage global-local graph optimization,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1150–1157, 2021

    2021

  13. [21]

    Kimera-multi: Robust, distributed, dense metric-semantic slam for multi-robot systems,

    Y . Tian, Y . Chang, F. H. Arias, C. Nieto-Granda, J. P. How, and L. Carlone, “Kimera-multi: Robust, distributed, dense metric-semantic slam for multi-robot systems,” IEEE Transactions on Robotics , vol. 38, no. 4, 2022

    2022

  14. [22]

    Lamp 2.0: A robust multi-robot slam system for operation in challenging large-scale underground environments,

    Y . Chang, K. Ebadi, C. E. Denniston, M. F. Ginting, A. Rosinol, A. Reinke, M. Palieri, J. Shi, A. Chatterjee, B. Morrell et al. , “Lamp 2.0: A robust multi-robot slam system for operation in challenging large-scale underground environments,” IEEE Robotics and Automation Lette...

    2022

  15. [23]

    Macim: Multi-agent collaborative implicit mapping,

    Y . Deng, Y . Tang, Y . Yang, D. Wang, and Y . Yue, “Macim: Multi-agent collaborative implicit mapping,” IEEE Robotics and Automation Letters, 2024

    2024

  16. [24]

    Distributed nerf learning for collaborative multi-robot perception,

    H. Zhao, B. Ivanovic, and N. Mehr, “Distributed nerf learning for collaborative multi-robot perception,” arXiv preprint arXiv:2409.20289, 2024

    2024 arXiv

  17. [25]

    Netvlad: Cnn architecture for weakly supervised place recognition,

    R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, “Netvlad: Cnn architecture for weakly supervised place recognition,” in Proceed- ings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 5297–5307

    2016

  18. [26]

    Superpoint: Self- supervised interest point detection and description,

    D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superpoint: Self- supervised interest point detection and description,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2018, pp. 224–236

    2018

  19. [27]

    Lightglue: Local feature matching at light speed,

    P. Lindenberger, P.-E. Sarlin, and M. Pollefeys, “Lightglue: Local feature matching at light speed,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 627–17 638

    2023

  20. [28]

    Superglue: Learning feature matching with graph neural networks,

    P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superglue: Learning feature matching with graph neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 4938–4947

    2020

  21. [29]

    Least-squares estimation of transformation parameters between two point patterns,

    S. Umeyama, “Least-squares estimation of transformation parameters between two point patterns,” IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. 04, pp. 376–380, 1991

    1991

  22. [30]

    Depth anything: Unleashing the power of large-scale unlabeled data,

    L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao, “Depth anything: Unleashing the power of large-scale unlabeled data,” in CVPR, 2024

    2024

  23. [31]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763

    2021

  24. [32]

    Extract free dense labels from clip,

    C. Zhou, C. C. Loy, and B. Dai, “Extract free dense labels from clip,” in European Conference on Computer Vision. Springer, 2022, pp. 696– 712

    2022

  25. [33]

    Zero-shot out-of- distribution detection based on the pre-trained model clip,

    S. Esmaeilpour, B. Liu, E. Robertson, and L. Shu, “Zero-shot out-of- distribution detection based on the pre-trained model clip,” in Proceed- ings of the AAAI conference on artificial intelligence , vol. 36, no. 6, 2022, pp. 6568–6576

    2022

  26. [34]

    Langsplat: 3d language gaussian splatting,

    M. Qin, W. Li, J. Zhou, H. Wang, and H. Pfister, “Langsplat: 3d language gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 051–20 060

    2024

  27. [35]

    Bilateral guided radiance field processing,

    Y . Wang, C. Wang, B. Gong, and T. Xue, “Bilateral guided radiance field processing,” ACM Transactions on Graphics (TOG), vol. 43, no. 4, pp. 1–13, 2024

    2024

  28. [36]

    Ros: an open-source robot operating system,

    M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y . Ng et al. , “Ros: an open-source robot operating system,” in ICRA workshop on open source software , vol. 3. Kobe, Japan, 2009, p. 5

    2009

  29. [37]

    Nerfbridge: Bring- ing real-time, online neural radiance field training to robotics,

    J. Yu, J. E. Low, K. Nagami, and M. Schwager, “Nerfbridge: Bring- ing real-time, online neural radiance field training to robotics,” arXiv preprint arXiv:2305.09761, 2023

    2023 arXiv

  30. [38]

    Kiss-icp: In defense of point-to-point icp–simple, accu- rate, and robust registration if done the right way,

    I. Vizzo, T. Guadagnino, B. Mersch, L. Wiesmann, J. Behley, and C. Stachniss, “Kiss-icp: In defense of point-to-point icp–simple, accu- rate, and robust registration if done the right way,” IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 1029–1036, 2023

    2023

  31. [39]

    Fast lossless depth image compression,

    A. D. Wilson, “Fast lossless depth image compression,” in Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces, 2017, pp. 100–105

    2017

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.