LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments

Liang Hu; Renxiang Xiao; Wei Liu; Yushuai Chen

REVIEW 4 major objections 7 minor 31 references

LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments

T0 review · 4 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read LiV-GS proposes the first SLAM system that directly aligns sparse LiDAR point clouds with continuous 3D Gaussian maps in large outdoor scenes, and it reports 7.98 FPS mapping with rendering quality that beats the compared baselines.

desk verdict A plausible LiDAR-camera 3DGS SLAM system with competitive reported numbers, but its headline claim of reconstructing beyond the LiDAR FOV is not yet backed by evidence. read the letter →

arxiv 2411.12185 v1 pith:NS2XSJD2 submitted 2024-11-19 cs.RO

Renxiang Xiao , Wei Liu , Yushuai Chen , Liang Hu This is my paper · ORCID

classification cs.RO

keywords 3DGaussianSplattingLiDAR-visualSLAMoutdoormappingnovelviewsynthesisconditionalconstraintGaussian-LiDARalignmentcross-modallocalizationlarge-scalereconstruction

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 proposes LiV-GS, a simultaneous localization and mapping system that builds a 3D Gaussian Splatting map from LiDAR and camera data in outdoor scenes. The authors claim it is the first method to align sparse, discrete LiDAR point clouds directly with continuous differentiable Gaussian maps, avoiding the fixed-resolution limitation of conventional LiDAR maps. They further claim that their conditional Gaussian constraint lets the map grow into regions where LiDAR has no measurements, so the reconstruction extends beyond the LiDAR field of view. If these claims hold, a robot could localize and produce photorealistic, geometrically usable maps in near real time from a single pass of a LiDAR-camera pair.

What carries the argument

The central object is the set of 3D Gaussian ellipsoids, each parameterized by opacity, color, mean, and covariance, used as the map and as the tracking target. Three mechanisms carry the argument: (1) covariance-based point-to-Gaussian matching with a density and normal-consistency weight W(x)=C(x)rho(x) and a normal-direction regularizer; (2) a normal loss that penalizes the variance along the Gaussian normal, keeping ellipsoids flat and surface-aligned; and (3) the Conditional Gaussian Constraint of Eq. (11), which propagates reliability from LiDAR-measured Gaussians to color-only Gaussians by sampling new ellipsoids from a normal distribution around the nearest reliable Gaussian. The constraint is the mechanism that extends reconstruction beyond the LiDAR field of view.

What would settle it

Measure the reconstructed surface in a region that LiDAR never observed and compare it against ground-truth geometry from a later dense LiDAR pass or a survey-grade scanner; if the conditional-Gaussian-split region deviates from the true surface by more than the in-field-of-view error, the central claim fails. A cheaper check is an ablation that removes the conditional Gaussian constraint and measures rendering quality on out-of-field-of-view image patches, since removing it should clearly degrade those regions if the constraint is doing the claimed work.

Watch

Extended reading notes

Core claim

LiV-GS treats a scene as a collection of 3D Gaussian ellipsoids and uses that same representation for both front-end tracking and back-end map optimization. LiDAR points are matched to Gaussian ellipsoids through shared covariance attributes, with a weighting function that combines local Gaussian density and normal consistency, plus a normal loss that keeps Gaussians flat along surfaces. The distinctive claim is the Conditional Gaussian Constraint: a color-supervised Gaussian outside the LiDAR field of view is pulled toward the nearest LiDAR-measured Gaussian by postulating p(X|Y=y) ~ N(mu_x(y), Sigma_y), and each split Gaussian is treated as reliable after one round of back-end optimization. This is what lets the map represent objects and regions never touched by LiDAR. The system reports 7.98 FPS average throughput and, on the tested sequences, generally lower trajectory error on low-speed sequences and better rendering metrics than the compared point-cloud, feature-based, and Gaussian Splatting SLAM baselines, and it demonstrates cross-modal radar localization on the resulting Gaussian map.

Load-bearing premise

The system relies on the assumption that a color-supervised Gaussian outside the LiDAR field of view is made reliable by pulling it toward the nearest LiDAR-measured Gaussian and treating split Gaussians as reliable after one round of back-end optimization; if that propagation does not preserve true surface geometry, the claimed reconstruction beyond the LiDAR field of view fails.

Editorial extensions

If this is right

A single LiDAR-camera pass can produce a photorealistic, differentiable map while simultaneously estimating the platform pose, removing the need for a separate offline reconstruction stage.
Because the map is continuous Gaussians rather than fixed-resolution point clouds, tracking and rendering are not bounded by LiDAR resolution, and areas between and beyond LiDAR beams can still be represented.
The rendering quality of the odometry output is close to the ground-truth-pose rendering, so the estimated trajectory is precise enough for appearance-based map use.
The Gaussian map carries enough geometric structure that a different modality such as mmWave radar can relocalize against it, suggesting the map is not only visually but geometrically consistent.
At 7.98 FPS with a 10 Hz LiDAR input, the pipeline approaches the LiDAR input rate, indicating potential for online use.

Reading between the lines

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

If the conditional Gaussian constraint propagation holds on longer and more varied sequences, it effectively turns visual texture into a geometric prior, letting color continuity supply depth hypotheses in textureless or far-field regions and reducing reliance on dense depth sensors.
The same Gaussian map could support downstream tasks the paper does not pursue, such as semantic segmentation or object-level editing, because each Gaussian is an individually optimizable primitive with spatial extent.
A natural next test is closed-loop and high-speed operation: the paper attributes degraded loop-closure and high-speed performance to the absence of loop closure and spherical harmonics, so adding those modules is a direct, falsifiable path to improvement.
Cross-modal radar localization on a Gaussian map suggests a testable extension: using the map as a shared representation for other sparse sensors in all-weather conditions.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: LiV-GS is a coherent system integration and the reported tracking/rendering numbers are competitive, but the one thing it claims over its closest relatives—reconstructing geometry beyond the LiDAR field of view—is not actually supported by the evidence in the paper. The Conditional Gaussian Constraint (Eq. 11) is a postulate, not a derived update rule, and there is no ablation or metric that isolates out-of-FOV regions. If that piece falls, the paper is a competent but largely incremental LiDAR-camera 3DGS SLAM.

What is genuinely new: combining covariance-based point-to-Gaussian alignment (from GS-ICP-SLAM-ish work) with a normal-consistency weight, a normal-length regularizer, and conditional propagation of Gaussians into regions without LiDAR depth. The system runs at 7.98 FPS and produces the lowest ATE on several NTU4DRadLM sequences, and the rendering metrics beat the baselines tested. That is real, if modest, progress.

Soft spots, in order of severity: - The central claim. Eq. (11) says given nearest LiDAR Gaussian y, color-supervised Gaussian x follows N(mu_x(y), Sigma_y). There is no derivation of this conditional, no statement of how it enters the loss, and no definition of 'reliable' beyond 'after one round of backend optimization.' At depth discontinuities—building edges, foliage, poles—a single nearest-neighbor covariance cannot encode whether the unobserved surface continues, is adjacent, or is detached. The only geometric validation, the radar relocalization, is qualitative and covers the LiDAR-supported portion of the map, not the extrapolated areas. This is exactly the load-bearing gap the stress-test note flags. - Evaluation gaps. No error bars. No comparison against MM-Gaussian, LIV-GaussMap, or Gaussian-LIC, the closest methods. Long sequences are segmented into 220–250 m chunks, which may help any frame-to-map tracker and avoids long-drift evaluation. - Minor: no code; the 7.98 FPS figure is a mean without variance; the skybox with 100,000 Gaussians is a detail that could hide cost.

The reader's concern about circularity is fair but not the main issue; this is a standard frame-to-map loop. The real problem is missing independent validation of the claimed advantage.

Who this is for: anyone working on LiDAR-visual fusion for 3DGS SLAM. It deserves a serious referee—the system is plausible and the NTU numbers are worth checking—but the referee should require an ablation that disables the CGC and a separate metric for out-of-FOV regions, plus comparisons with the omitted related methods.

Referee Report

4 major / 7 minor

Summary. The manuscript presents LiV-GS, a LiDAR-visual SLAM system that uses 3D Gaussian splatting as the map representation for outdoor scenes. The front end tracks by aligning LiDAR point clouds with Gaussian centers using covariance and normal constraints; the back end optimizes poses and map parameters with photometric, geometric, and normal losses. A "Conditional Gaussian Constraint" (CGC) is introduced to propagate information from LiDAR-supported Gaussians to color-only Gaussians, aiming to reconstruct areas beyond the LiDAR field of view. Experiments on six sequences from NTU4DRadLM and one loop-closure sequence from R3LIVE report trajectory and rendering comparisons against several open-source baselines, plus a qualitative cross-modal radar relocalization result.

Significance. If the results hold, LiV-GS would be a useful contribution: it demonstrates that a 3DGS representation can support real-time LiDAR-visual SLAM outdoors, with rendering quality and tracking accuracy competitive with or better than several existing open-source systems. The reported 7.98 FPS processing rate is a concrete practical claim, and the cross-modal radar localization experiment is an interesting downstream use of a Gaussian map. However, the central claimed novelty—high-quality reconstruction beyond the LiDAR field of view—rests entirely on the CGC, whose validity is not quantitatively demonstrated. The significance of the paper therefore depends on closing that evidence gap and on comparing directly with the closest related LiDAR-visual 3DGS SLAM methods, which are only summarized in a table, not benchmarked.

major comments (4)

[III-D, Eq. (11)] The Conditional Gaussian Constraint is the load-bearing component for the paper's main claimed advantage over prior work (reconstruction beyond the LiDAR field of view, as stated in Table I and the abstract), but it is presented as a postulate with no derivation, no update rule, and no mechanism connecting it to the loss in Eq. (10). The paper states that a split Gaussian becomes reliable after one round of backend optimization, but this reliability criterion is not formally defined. The only evidence for the out-of-FOV reconstruction is qualitative Fig. 4. I request an ablation that removes the CGC, and a quantitative evaluation on pixels or rays whose LiDAR depth is unavailable, comparing in-FOV and out-of-FOV reconstruction quality. Without this, the claimed advantage is unsupported.
[Table II and Table III] The closest related methods—DrivingGaussian, LIV-GaussMap, and MM-Gaussian, which are listed in Table I as the most relevant LiDAR-visual 3DGS fusion works—are not included in any quantitative comparison. Since these methods also fuse LiDAR point clouds with Gaussian maps and optimize poses, excluding them makes it impossible to verify the claim of superiority over the state of the art that this paper itself identifies. Please add quantitative trajectory and rendering comparisons with at least the open-source methods among them (e.g., MM-Gaussian), or clearly justify their exclusion if their public implementations are unavailable on the same platform.
[IV-B and IV-C] The evaluation protocol has two issues that affect the reliability of the reported improvements. First, long sequences are segmented into shorter segments (e.g., 2100–2400 images) without reporting results on the full trajectories; this can favor methods that drift substantially over long distances, and it is unclear how the segment boundaries were chosen. Second, no error bars or multiple-run statistics are reported for any trajectory or rendering metric. Since several baseline methods are stochastic (e.g., MonoGS, SplaTAM) or initialization-sensitive, single-run comparisons are not sufficient to support the claim of superior or consistent performance. Please report mean/std over at least three runs, or state if the methods are deterministic and provide a rationale.
[IV-E and Fig. 7] The runtime claim of 7.98 FPS is presented as evidence of real-time capability, but the system uses an asynchronous communication mechanism and the stated module times (0.07 ms, 0.04 ms, 0.09 ms) are implausibly small compared with the reported overall frame rate. It is unclear whether 7.98 FPS is measured under the asynchronous pipeline with all modules running concurrently, or as a throughput on a single thread. Please clarify the measurement protocol and report the actual wall-clock time per frame, as this claim is central to the abstract's "fast and accurate" characterization.

minor comments (7)

[III-A] The module name "Data Preporessing" in Fig. 1 is a typo; it should read "Data Preprocessing."
[Abstract] The sentence "integrates the normal orientation into the loss function to refines the Gaussian map" has a grammar error; "refines" should be "refine."
[III-B, Eq. (3)] The regularization term R is described as being multiplied by a hyperparameter, but no such hyperparameter appears in Eq. (3). Please define the full expression with its weight, and specify its value in the implementation details.
[III-B] The term "local spherical region Nr(x)" is used in the density function definition, but the radius r is not specified. Please state how r is chosen or adapted.
[IV-C] Table II reports the rotational error in units of degrees per 100 m, but the units are not stated in the table caption. Please add the units to avoid ambiguity.
[IV-F] The radar relocalization experiment is described as qualitative; Fig. 8 shows only the trajectory. Please provide quantitative relocalization error (e.g., ATE against the ground-truth trajectory) to substantiate the claim of accurate cross-modal localization.
[References] Some references are cited as arXiv preprints without version or DOI information (e.g., [4], [5], [10], [11], [14], [17], [21], [23], [29], [31]). This is acceptable for a preprint, but please update to published versions where available.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SLAM pipeline is self-consistent, the CGC is an explicit modeling assumption, and the noted evaluation gaps are validation issues rather than circular reductions.

full rationale

No load-bearing circular step is present. The front-end tracking aligns LiDAR point clouds with the Gaussian map built from prior frames (Eq. 3), and the back-end optimizes poses and the Gaussian map with photometric, geometric, and normal losses (Eq. 10); this is standard SLAM self-consistency, where the map used for tracking is the same map being refined, but the trajectory and map outputs are not defined in terms of one another. The Conditional Gaussian Constraint in Eq. (11) is stated as a postulate, p(X|Y=y) ~ N(mu_x(y), Sigma_y), for color-supervised Gaussians outside the LiDAR field of view. It is not derived from the loss, but it is also not equivalent to the paper's claimed outcome; the qualitative claim that this produces geometrically useful beyond-FOV reconstruction is an empirical assertion that is under-validated, not a conclusion forced by construction. Similarly, rendering metrics in Table III are computed on the optimized viewpoints used for mapping, so they measure fit rather than held-out novel-view generalization; this weakens the 'novel view synthesis' terminology but is not a fitted parameter renamed as a prediction. References that may share an author (e.g., EfficientGS [13]) are used only as related work and are not load-bearing. The paper's central derivation is therefore self-contained, with the main weaknesses being missing ablations and qualitative-only validation of the extrapolated regions.

Assumptions & free parameters 6 free parameters · 5 assumptions · 1 invented entities

The central claim rests on several unverified modeling choices: the covariance matching heuristic, the conditional Gaussian propagation, and the use of R3LIVE as ground truth. None of these are machine-checked or reproduced with released code. The free parameters are mostly unreported hyperparameters, which adds to the difficulty of independent verification.

free parameters (6)

lambda1 and lambda2 in loss Eq. (10) = not reported
Weights balancing photometric, geometric, and normal losses in the backend optimization; no values or sensitivity analysis are given.
regularization weight for R in Eq. (3) = not reported
Multiplies the normal-direction regularization term in tracking; the paper says it is a hyperparameter but never reports its value.
local spherical radius r in density Eq. (5) = not reported
Defines the neighborhood for computing Gaussian density around each LiDAR point; chosen by hand.
co-visibility keyframe threshold = not reported
Determines when a frame becomes a keyframe; the threshold is not specified.
sliding window length for tracking = 10 frames
Filters Gaussians from the last 10 time steps for frame-to-map matching, a fixed hand-set constant.
skybox Gaussian count = 100,000
Number of Gaussian primitives initialized outside the scene for sky representation, chosen ad hoc.

assumptions (5)

domain assumption Point clouds and Gaussian ellipsoids can be matched through shared covariance attributes, making the error function in Eq. (3) a valid pose estimator.
Core tracking premise in Section III-B; not demonstrated for sparse outdoor LiDAR with variable density.
ad hoc to paper Conditional Gaussian constraint: p(X|Y=y) ~ N(mu_x(y), Sigma_y) for color-supervised Gaussians near LiDAR-measured Gaussians.
Postulated in Section III-D without derivation or validation; the entire out-of-FOV reconstruction capability depends on it.
domain assumption R3LIVE trajectories are accurate enough to serve as ground truth for ATE and RE evaluation.
Used in Section IV-A; errors in R3LIVE would bias all reported trajectory numbers.
standard math The approximation in Eq. (7) preserves enough information for tracking while simplifying the density computation.
Replaces a full covariance distance with a normal-aligned term for speed, but the accuracy impact is not measured.
domain assumption Gaussians initialized from keyframe point clouds with ray-extended covariance provide a sufficient starting map.
Stated in Section III-B; no failure-case analysis for initialization.

invented entities (1)

'Reliable Gaussian' label
purpose: Identifies Gaussians with LiDAR support that can seed conditional propagation into regions without LiDAR measurements
Introduced in Section III-D; reliability is asserted from proximity to LiDAR points and never quantitatively verified.

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Cite this review

Pith. "Pith review of LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments." pith.science (2026). https://pith.science/paper/NS2XSJD2

@misc{pith2026241112185,
  author       = {Pith},
  title        = {Pith review of: LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NS2XSJD2}},
  note         = {Machine review of arXiv:2411.12185}
}

read the original abstract

We present LiV-GS, a LiDAR-visual SLAM system in outdoor environments that leverages 3D Gaussian as a differentiable spatial representation. Notably, LiV-GS is the first method that directly aligns discrete and sparse LiDAR data with continuous differentiable Gaussian maps in large-scale outdoor scenes, overcoming the limitation of fixed resolution in traditional LiDAR mapping. The system aligns point clouds with Gaussian maps using shared covariance attributes for front-end tracking and integrates the normal orientation into the loss function to refines the Gaussian map. To reliably and stably update Gaussians outside the LiDAR field of view, we introduce a novel conditional Gaussian constraint that aligns these Gaussians closely with the nearest reliable ones. The targeted adjustment enables LiV-GS to achieve fast and accurate mapping with novel view synthesis at a rate of 7.98 FPS. Extensive comparative experiments demonstrate LiV-GS's superior performance in SLAM, image rendering and mapping. The successful cross-modal radar-LiDAR localization highlights the potential of LiV-GS for applications in cross-modal semantic positioning and object segmentation with Gaussian maps.

Figures

Figures reproduced from arXiv: 2411.12185 by the authors.

**Figure 1.** Overview of the system: The SLAM system comprises a tracking and optimization process that together support the visual representation of the Gaussian map. The map update process uses LiDAR depth and color supervision to adjust the new Gaussians. point clouds by transforming the visual perspective into BEV and employing an encode-decode strategy for efficient processing. Octree-GS [12] utilizes the octree data struct… view at source ↗

**Figure 2.** Relationship between Density and weight: Gaussians based on only color supervision result in isotropic and sparse Gaussians (top left). Regions with dense depth input from LiDAR typically show Gaussians in higher density (bottom left). A. System overview As depicted in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Effect of Splitting via conditional Gaussian constraints (CGC). Our approach enhances the representation of Gaussians for objects in the images that lack LiDAR depth input via the introduced CGC. where di and ci represent the distance and color to the Gaussian gi along the camera ray. The loss function used for optimizing the parameters of Gussians is designed as: L = (1 − λ1)Epho + λ1Egeo + λ2Enormal (10) where th… view at source ↗

Figures from the paper (4 more)

**Figure 5.** Figure 5: Comparison of trajectories using different SLAM algorithms on four sequences of NTU4DRadLM dataset. algorithm which integrates vision, LiDAR, and IMU data, is used as the ground truth. As the trajectory accuracy, LiV-GS is compared with the established point cloudsbas…

**Figure 6.** Figure 6: Comparison of Rendering Results. B. Datasets To effectively evaluate our LiV-GS, we utilized the opensource large-scale dataset NTU4DRadLM, which includes the data collected by three different types of sensors: LivoxHorizon LiDAR at 10Hz, a monocular camera with a re…

**Figure 8.** Figure 8: Visualization of cross-modal mmWave radar localization trajectory. mmWave radar localization on the Gaussian map. Unlike LiDAR, the point clouds of mm-Wave radar are much sparser and have lower resolution and higher noises. Robust cross-modal radar localization is pos…

**Figure 7.** Figure 7: Comparison of performance metrics and system runtime. FPS is calculated as the ratio of the total number of processed frames to the total time. A value of 7.98 FPS means that on average 7.98 depth maps and RGB images can be processed in one second. E. Evaluation betwee…

Discussion (0). Continue with ORCID to comment.

Reference graph

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Z. Zhang and D. Scaramuzza, “A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry,” in IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS) , 2018

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R3LIVE: A Robust, Real-Time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package,

J. Lin and F. Zhang, “R3LIVE: A Robust, Real-Time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package,” in 2022 International Conference on Robotics and Automa- tion (ICRA). IEEE, 2022, pp. 10 672–10 678

2022
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A Portable Three-Dimensional LiDAR-based system for long-term and wide-area people behavior measurement,

K. Koide, J. Miura, and E. Menegatti, “A Portable Three-Dimensional LiDAR-based system for long-term and wide-area people behavior measurement,” International Journal of Advanced Robotic Systems , vol. 16, no. 2, p. 1729881419841532, 2019

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ORB-SLAM3: An accurate open-source library for visual, visual–inertial, and multimap SLAM,

C. Campos, R. Elvira, J. J. G. Rodr ´ıguez, J. M. Montiel, and J. D. Tard´os, “ORB-SLAM3: An accurate open-source library for visual, visual–inertial, and multimap SLAM,” IEEE Transactions on Robotics, vol. 37, no. 6, pp. 1874–1890, 2021

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Gaussian-SLAM: Photo-realistic dense SLAM with Gaussian Splatting,

V . Yugay, Y . Li, T. Gevers, and M. R. Oswald, “Gaussian-SLAM: Photo-realistic dense SLAM with Gaussian Splatting,” arXiv preprint arXiv:2312.10070, 2023

2023 arXiv
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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,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 357–21 366

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NeRF++: An- alyzing and improving Neural Radiance Fields,

K. Zhang, G. Riegler, N. Snavely, and V . Koltun, “NeRF++: An- alyzing and improving Neural Radiance Fields,” arXiv preprint arXiv:2010.07492, 2020

2010 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

NeRF: Representing Scenes as Neural Radiance Fields for view synthesis,

B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoor- thi, and R. Ng, “NeRF: Representing Scenes as Neural Radiance Fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021

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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 Trans. Graph., vol. 42, no. 4, pp. 139–1, 2023

work page 2023

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NeRF-LOAM: Neural Implicit Representation for Large-scale incremental LiDAR odometry and mapping,

J. Deng, Q. Wu, X. Chen, S. Xia, Z. Sun, G. Liu, W. Yu, and L. Pei, “NeRF-LOAM: Neural Implicit Representation for Large-scale incremental LiDAR odometry and mapping,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8218–8227

work page 2023

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RGBD Gs-icp SLAM,

S. Ha, J. Yeon, and H. Yu, “RGBD Gs-icp SLAM,” arXiv preprint arXiv:2403.12550, 2024

arXiv 2024

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SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection

Y . Tao, Y . Bhalgat, L. F. T. Fu, M. Mattamala, N. Chebrolu, and M. Fallon, “SiLVR: Scalable LiDAR-Visual Reconstruction with Neural Radiance Fields for robotic inspection,” arXiv preprint arXiv:2403.06877, 2024

work page Pith review arXiv 2024

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GS-SLAM: Dense visual SLAM with 3D Gaussian Splatting,

C. Yan, D. Qu, D. Xu, B. Zhao, Z. Wang, D. Wang, and X. Li, “GS-SLAM: Dense visual SLAM with 3D Gaussian Splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 19 595–19 604

work page 2024

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MM3DGS SLAM: Multi-Modal 3D Gaussian Splat- ting for SLAM Using Vision, Depth, and Inertial Measurements,

L. C. Sun, N. P. Bhatt, J. C. Liu, Z. Fan, Z. Wang, T. E. Humphreys, and U. Topcu, “MM3DGS SLAM: Multi-Modal 3D Gaussian Splat- ting for SLAM Using Vision, Depth, and Inertial Measurements,” arXiv preprint arXiv:2404.00923 , 2024

arXiv 2024

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MVS-SLAM: Enhanced Multiview geometry for improved Semantic RGBD SLAM in dynamic environment,

Q. U. Islam, H. Ibrahim, P. K. Chin, K. Lim, and M. Z. Abdullah, “MVS-SLAM: Enhanced Multiview geometry for improved Semantic RGBD SLAM in dynamic environment,” Journal of Field Robotics , vol. 41, no. 1, pp. 109–130, 2024

work page 2024