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REVIEW 4 major objections 6 minor 23 references

A Gaussian-splatting SLAM pipeline whose prior-image masks and adaptive feature selection keep photorealistic dense mapping accurate and real-time in scenes with moving objects.

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DyPho-SLAM uses prior-image masks and adaptive feature selection to keep camera tracking accurate while building a photorealistic static 3D map in real time.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection DyPho-SLAM is a credible real-time extension of Photo-SLAM for dynamic scenes, with two genuinely new mechanisms and good TUM tracking numbers, but the mapping claims are only qualitative, no code is available, and the mask-refinement loop is never stress-tested. the 4 major comments →

arxiv 2509.00741 v1 pith:DOZGNKSM submitted 2025-08-31 cs.RO

DyPho-SLAM : Real-time Photorealistic SLAM in Dynamic Environments

Yi Liu , Keyu Fan , Bin Lan , Houde Liu This is my paper
classification cs.RO
keywords dynamic SLAM3D Gaussian SplattingRGB-D SLAMphotorealistic dense mappingdynamic object maskingprior image modeladaptive feature selectioncamera tracking
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

DyPho-SLAM is a visual SLAM system that tries to settle a practical question: can a robot or AR device build a photorealistic 3D map of a room at interactive speed while people walk through it? The paper's answer is yes, provided moving pixels are kept out of both the pose solver and the Gaussian map. It removes dynamic objects with masks refined through a running prior-image background model, and it counteracts the resulting loss of features with an adaptive feature-selection threshold. On four dynamic TUM sequences it reports a 1.6 cm mean absolute trajectory error at 16.08 frames per second, below the dense and feature-based baselines it tabulates. If correct, this closes the gap between accurate tracking and high-fidelity online mapping in human-filled indoor scenes.

Core claim

The central claim is that explicit 3D Gaussian Splatting mapping can be made real-time and drift-resistant in dynamic scenes by (1) building a static-background depth model that recursively blends the previous background estimate, depth rendered from the Gaussian map at a constant-velocity predicted pose, and the current depth gated by semantic and motion masks; (2) classifying each pixel as static only if its neighborhood matches that background model; and (3) feeding the pose optimizer an adaptive set of feature points whose threshold tightens as the masked area grows, so removing dynamics never starves the bundle adjustment. The loss that updates the Gaussians is masked in both color and

What carries the argument

The load-bearing object is the recursive prior static-background model, Eq. (5), a running estimate of the scene's static depth updated from three sources: the previous estimate, depth rendered by the Gaussian map at a pose predicted by a constant-velocity model (Eq. 6), and the current depth masked by segment and optical-flow masks. The final mask, Eq. (7), flags a pixel as static only when its neighboring depths agree with this background model, and the adaptive feature threshold of Eq. (9), which shrinks as the masked fraction of the image grows, rebuilds the feature pool in masked scenes. This background model is what turns single-frame masks into a temporally coherent filter, and becaus

Load-bearing premise

The prior background model is updated from depth rendered by the Gaussian map at a constant-velocity predicted pose, so the mask's quality depends on the very tracking and map it is protecting: if the camera decelerates sharply or the pose slips for a few frames, the rendered depth misaligns and clean static pixels can be dropped while moving pixels slip through.

What would settle it

Run a dynamic TUM sequence while replacing the constant-velocity pose prediction in Eq. (6) with the ground-truth relative pose at a chosen frame, and compare the resulting mask against a ground-truth static/dynamic label map at that instant: a spike in misclassified pixels, or a measurable drop in downstream ATE when the constant-velocity prediction is used, would confirm the mask's pose-dependence as the limiting assumption.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Photorealistic dense SLAM need not be a trade-off with dynamic-scene accuracy: the masked-loss Gaussian map and the feature-compensated tracker coexist at 16 Hz on a consumer GPU.
  • Dynamic objects can be excluded from the map without explicit per-object tracking or motion models; a background-prior mask plus masked gradients suffices for static map fidelity.
  • In dynamic scenes, a dense Gaussian-splatting pipeline can match or beat the trajectory accuracy of sparse feature-based pipelines, since the mask protects the pose solver from moving-point outliers.
  • The two proposed modules are complementary: combined they reach 1.62 cm and 2.55 cm ATE on the two ablation sequences, each better than either module alone.
  • Because the map is static-only by construction, downstream consumers such as planners or manipulators inherit a clean static model of the environment at real-time rates.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The mask is pose-dependent in an uncomfortable way: the background reference depth is rendered from the Gaussian map at a constant-velocity predicted pose, so a tracking blip or sharp deceleration can misclassify static texture as dynamic or let moving pixels through, exactly when the mask is most needed. A clean test would inject a known pose perturbation at a chosen frame and measure the resulti
  • The authors state that the map is static-background only; the natural next step, which they flag, is a second Gaussian layer for dynamic objects. If the background-prior masking is as reliable as reported, it supplies a ready-made segmentation signal for initializing per-object Gaussians.
  • The adaptive feature threshold scales only with masked area; coupling it to the optimizer's own health (for example, the inlier ratio of the bundle adjustment) could make the feature pool respond to tracking difficulty rather than just scene content.
  • Reproducibility note: the in-text citation [16] is described as the YOLO segment network used in Eq. (2), but the reference-list entry under that marker is a different paper; the segment-mask component's published source needs verification. This does not affect the other components.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes DyPho-SLAM, a real-time RGB-D SLAM system based on 3D Gaussian Splatting for dynamic environments. Its main algorithmic contributions are (i) a refined dynamic-object mask that combines semantic, optical-flow, and a 'prior image' background depth model updated with rendered depth, and (ii) an adaptive feature extraction strategy that compensates for the reduced number of static features. The system is evaluated on TUM RGB-D dynamic sequences, with a qualitative comparison on Bonn sequences, an ablation study, and a runtime comparison. The reported mean ATE is 1.6 cm on four TUM sequences, with 16.08 Hz processing, and the paper claims state-of-the-art pose accuracy and photorealistic static mapping.

Significance. If the results hold, the paper would make a useful contribution: it demonstrates that a Gaussian-Splatting SLAM system can achieve competitive pose accuracy in dynamic scenes while maintaining a dense photorealistic map. The use of a prior background model to refine masks is a sensible idea, and the ablation in Table II indicates that both proposed modules improve ATE. The inclusion of several recent dynamic 3DGS baselines in Table I is also helpful. However, the significance is currently limited by evaluation gaps: the mapping-quality claim rests on qualitative renderings only, no Bonn numbers are reported despite Bonn being claimed, and no repeated runs or error bars are provided.

major comments (4)
  1. [§III-A, Eq. (5)–(7)] The core mask-refinement mechanism has a feedback loop that is not stress-tested. Eq. (5) updates the background depth model B_i using Gaussian-rendered depth R_d(G, T^{i-1}δT), where the predicted pose is derived from a constant-velocity model (Eq. (6)). Eq. (7) then classifies pixels as static based on agreement with B_i, and this mask gates feature extraction, pose optimization (Eq. (10)), and Gaussian insertion/updating (Eq. (14)). If tracking is briefly wrong or the camera decelerates sharply, R_d is misaligned and the mask can reject static background—removing exactly the features needed for pose correction. Conversely, if a moving object is ever inserted into the Gaussian map, it can be absorbed into B_i and later self-validated. The paper provides no sensitivity analysis, mask-quality ablation, or injected-pose-error experiment to bound this risk. Since the SOTA pose and mapping
  2. [§IV-A/B, Table I, Fig. 4] The paper claims in the Introduction and Contributions that experiments are conducted on two dynamic datasets (TUM and Bonn), but all quantitative tracking results are for TUM only. Bonn appears only as qualitative rendering examples in Fig. 4. Likewise, the mapping-quality claim ('state-of-the-art ... dense map reconstruction') is supported only by qualitative renderings; no PSNR/SSIM/LPIPS or similar metric is reported for reconstruction. In addition, Table I contains internal inconsistencies: for SplaTAM, the reported average ATE 135.5 cm is inconsistent with the column values (136.6, 185.4, 78.3, 14.1), and for ORB-SLAM3 the average ATE 15.8 cm does not match the listed per-sequence values. The evaluation should be completed with quantitative mapping metrics, Bonn results, and repeated runs with error bars; the table averages should be corrected or explained.
  3. [§IV-C, Table III] The 'real-time' claim needs clarification. Table III reports that DyPho-SLAM processes at 16.08 Hz (total operation approximately 64.9 ms/frame). The TUM RGB-D dataset is commonly recorded at 30 Hz, so processing at 16 Hz would not keep up with the sensor frame rate unless only keyframes are processed or frames are dropped. Moreover, the system is slower than the Photo-SLAM baseline in both tracking (61.7 vs 51.5 ms) and mapping (3.2 vs 2.5 ms) while achieving 16.08 vs 17.53 Hz FPS. The paper should state the definition of real-time used, specify whether every frame is processed, and justify the FPS claim relative to the dataset frame rate.
  4. [§IV-A, Eq. (5)] Several hyperparameters introduced in the method are not reported. Eq. (5) depends on τ and ρ, and Eq. (4) depends on optical-flow threshold T, but Section IV-A lists only nf, nm, k, σm, σ0, and λ. These values are necessary for reproducibility, since the prior-image mask is a central contribution. Please report all parameters, including their units/sensitivity if applicable.
minor comments (6)
  1. [Fig. 2 caption] Typo in caption: 'Pineline' should be 'Pipeline'.
  2. [Table III] The method name 'Dyna-SLAM3' is inconsistent with 'Dyna-SLAM' used in the text and Table I.
  3. [Eq. (8)] The symbol G is used both for the Gaussian map (Eq. (1), Eq. (14)) and for the grayscale image in Eq. (8). Please use a different symbol, e.g., I_gray, to avoid confusion.
  4. [References [16]] The text says the segment mask is implemented with a YOLO network, but reference [16] is cited as 'Ngd-slam: Towards real-time SLAM for dynamic environments without GPU'. This appears to be the wrong reference for YOLO; please cite the YOLO paper or the specific implementation used.
  5. [Eq. (5)] The term D_i(1 − MF_low M_MSegment) is ambiguous because the product of two masks is not clearly parenthesized, and the 'assign process L' mentioned in the text is not formally defined or used in the equation. Please clarify the composition of the raw mask.
  6. [Table I] For rows with missing values (e.g., NICE-SLAM, Dyna-SLAM), the averaging convention should be stated explicitly (e.g., average over available sequences only).

Circularity Check

0 steps flagged

No derivation-level circularity; reported pose accuracy is benchmark-validated externally.

full rationale

The paper's central claims—ATE on TUM/Bonn datasets and real-time performance—are evaluated against external ground-truth trajectories and baselines, not derived from the method's own fitted quantities. The mask-generation equations (5)–(7) do form an online feedback loop: the background depth model B_i is updated from Gaussian-rendered depth R_d(G, T^{i-1}δT), and the resulting mask M then gates the Gaussian update loss in Eq. (14). This is a genuine robustness concern, because a tracking error or a missed dynamic object can propagate into the mask and be masked out of the loss, but it is not a circular derivation in the sense of Eq. X being equivalent to Eq. Y by construction. No parameter is fitted to a subset of data and then 'predicted' as an independent result; the hyperparameters (τ, ρ, σ_m, etc.) are fixed constants, and ATE is measured against ground truth. The paper contains no load-bearing self-citations: the cited works (ORB-SLAM3, Photo-SLAM, YOLO, 3DGS) are used as components or baselines, and no uniqueness theorem is imported from the authors' prior work. The conclusion explicitly acknowledges the mask is 'still constrained by the underlying raw object detection' (Sec. V), which is a limitation of the feedback loop but not an admission of circularity. Therefore, no specific reduction can be exhibited, and the circularity score is 0.

Axiom & Free-Parameter Ledger

9 free parameters · 4 axioms · 0 invented entities

The central claim depends on roughly nine hand-set thresholds and weights, two of which are not even reported, plus assumptions about pretrained segmentation, optical flow, constant-velocity pose prediction, and Gaussian rendering. These are ordinary currencies of a SLAM systems paper, but the unreported values and the incorrect YOLO citation make the contribution harder to audit.

free parameters (9)
  • tau (prior-rendering trust) = not reported
    Belief weight for past renderings in background model update Eq. (5); hand-set but value not given in Section IV-A.
  • rho (mask trust) = not reported
    Trust weight for segment and motion masks in Eq. (5); hand-set but value not given.
  • nf = 12
    Feature-point count threshold in Eq. (8).
  • nm = 9
    Neighbor count threshold in static-depth test Eq. (7).
  • k = 0.9
    Adjusts adaptive threshold sigma_dy from masked-pixel ratio in Eq. (9).
  • sigma_m = 0.2
    Depth-difference threshold for static background comparison in Eq. (7).
  • sigma_0 = 0.3
    Initial gray-difference threshold in Eq. (9).
  • lambda = 0.7
    Color/depth loss weight in Eq. (14).
  • T (optical flow magnitude threshold) = not reported
    Threshold in Eq. (4) for motion mask; value not specified.
axioms (4)
  • domain assumption A pretrained YOLO semantic segmentation network produces reliable dynamic-object masks (f_theta).
    Invoked in Eq. (2) and Section III-A.1; no fine-tuning or failure analysis on the test sets is given.
  • domain assumption Optical flow from the brightness constraint detects moving objects independently of semantic class.
    Used to build the motion mask in Eqs. (3)-(4), but the flow method and threshold T are unspecified.
  • domain assumption The constant-velocity model deltaT = T_{i-1} T_{i-2}^{-1} predicts the current pose accurately enough for updating the background depth model.
    Appears in Eq. (6); if the camera decelerates sharply, the rendered depth used in Eq. (5) is misaligned.
  • standard math Gaussian Splatting rendering and bundle adjustment are reliable background tools.
    Standard rendering and optimization foundations from prior work [9], [17].

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of DyPho-SLAM : Real-time Photorealistic SLAM in Dynamic Environments." pith.science (2026). https://pith.science/paper/DOZGNKSM

@misc{pith2026250900741,
  author       = {Pith},
  title        = {Pith review of: DyPho-SLAM : Real-time Photorealistic SLAM in Dynamic Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DOZGNKSM}},
  note         = {Machine review of arXiv:2509.00741}
}
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read the original abstract

Visual SLAM algorithms have been enhanced through the exploration of Gaussian Splatting representations, particularly in generating high-fidelity dense maps. While existing methods perform reliably in static environments, they often encounter camera tracking drift and fuzzy mapping when dealing with the disturbances caused by moving objects. This paper presents DyPho-SLAM, a real-time, resource-efficient visual SLAM system designed to address the challenges of localization and photorealistic mapping in environments with dynamic objects. Specifically, the proposed system integrates prior image information to generate refined masks, effectively minimizing noise from mask misjudgment. Additionally, to enhance constraints for optimization after removing dynamic obstacles, we devise adaptive feature extraction strategies significantly improving the system's resilience. Experiments conducted on publicly dynamic RGB-D datasets demonstrate that the proposed system achieves state-of-the-art performance in camera pose estimation and dense map reconstruction, while operating in real-time in dynamic scenes.

Figures

Figures reproduced from arXiv: 2509.00741 by Bin Lan, Houde Liu, Keyu Fan, Yi Liu.

Figure 1
Figure 1. Figure 1: An example compares DyPho-SLAM with Photo-SLAM (Feature-GS) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Pineline of our Dypho-SLAM: the “Dynamic Process” module [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Camera trajectories estimated by Photo-SLAM and ours in the TUM dataset sequences, and the differences from ground truth [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of rendered results from state-of-the-art Gaussian Splatting SLAM methods. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

23 extracted references · 19 canonical work pages · 3 internal anchors

  1. [1]

    A survey of state-of-the-art on visual slam,

    Iman Abaspur Kazerouni, Luke Fitzgerald, Gerard Dooly, and Daniel Toal, “A survey of state-of-the-art on visual slam,” Expert Systems with Applications, vol. 205, pp. 117734, 2022

    work page 2022

  2. [2]

    How nerfs and 3d gaussian splatting are reshaping slam: a survey,

    Fabio Tosi, Youmin Zhang, Ziren Gong, Erik Sandstr ¨om, Stefano Mattoccia, Martin R Oswald, and Matteo Poggi, “How nerfs and 3d gaussian splatting are reshaping slam: a survey,” arXiv preprint arXiv:2402.13255, vol. 4, 2024

    Pith/arXiv arXiv 2024

  3. [3]

    NID-SLAM: Neural Implicit Representation-based RGB-D SLAM in dynamic environments

    Ziheng Xu, Jianwei Niu, Qingfeng Li, Tao Ren, and Chen Chen, “Nid-slam: Neural implicit representation-based rgb-d slam in dynamic environments,” arXiv preprint arXiv:2401.01189 , 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  4. [4]

    A survey of visual slam in dynamic environment: the evolution from geo- metric to semantic approaches,

    Yanan Wang, Yaobin Tian, Jiawei Chen, Kun Xu, and Xilun Ding, “A survey of visual slam in dynamic environment: the evolution from geo- metric to semantic approaches,” IEEE Transactions on Instrumentation and Measurement, 2024

    work page 2024

  5. [5]

    Dm- slam: A feature-based slam system for rigid dynamic scenes,

    Junhao Cheng, Zhi Wang, Hongyan Zhou, Li Li, and Jian Yao, “Dm- slam: A feature-based slam system for rigid dynamic scenes,” ISPRS International Journal of Geo-Information , vol. 9, no. 4, pp. 202, 2020

    work page 2020

  6. [6]

    Orb-slam3: An accurate open-source li- brary for visual, visual–inertial, and multimap slam,

    Carlos Campos, Richard Elvira, Juan J G ´omez Rodr ´ıguez, Jos ´e MM Montiel, and Juan D Tard ´os, “Orb-slam3: An accurate open-source li- brary for visual, visual–inertial, and multimap slam,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021

    work page 2021

  7. [7]

    Dynaslam: Tracking, mapping, and inpainting in dynamic scenes,

    Berta Bescos, Jos ´e M F ´acil, Javier Civera, and Jos ´e Neira, “Dynaslam: Tracking, mapping, and inpainting in dynamic scenes,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 4076–4083, 2018

    work page 2018

  8. [8]

    Rodyn-slam: Robust dynamic dense rgb-d slam with neural radiance fields,

    Haochen Jiang, Yueming Xu, Kejie Li, Jianfeng Feng, and Li Zhang, “Rodyn-slam: Robust dynamic dense rgb-d slam with neural radiance fields,” IEEE Robotics and Automation Letters , 2024

    work page 2024

  9. [9]

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

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis, “3d gaussian splatting for real-time radiance field rendering.,” ACM Trans. Graph. , vol. 42, no. 4, pp. 139–1, 2023

    work page 2023

  10. [10]

    3d gaussian splatting in robotics: A survey,

    Siting Zhu, Guangming Wang, Dezhi Kong, and Hesheng Wang, “3d gaussian splatting in robotics: A survey,” arXiv preprint arXiv:2410.12262, 2024

    Pith/arXiv arXiv 2024

  11. [11]

    Gs-slam: Dense visual slam with 3d gaussian splatting,

    Chi Yan, Delin Qu, Dan Xu, Bin Zhao, Zhigang Wang, Dong Wang, and Xuelong 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. 19595–19604

    work page 2024

  12. [12]

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

    Nikhil Keetha, Jay Karhade, Krishna Murthy Jatavallabhula, Geng- shan Yang, Sebastian Scherer, Deva Ramanan, and Jonathon 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. 21357–21366

    work page 2024

  13. [13]

    Photo- slam: Real-time simultaneous localization and photorealistic mapping for monocular stereo and rgb-d cameras,

    Huajian Huang, Longwei Li, Hui Cheng, and Sai-Kit Yeung, “Photo- slam: Real-time simultaneous localization and photorealistic mapping for monocular stereo and rgb-d cameras,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21584–21593

    work page 2024

  14. [14]

    Dgs- slam: Gaussian splatting slam in dynamic environment,

    Mangyu Kong, Jaewon Lee, Seongwon Lee, and Euntai Kim, “Dgs- slam: Gaussian splatting slam in dynamic environment,” arXiv preprint arXiv:2411.10722, 2024

    Pith/arXiv arXiv 2024

  15. [15]

    Dg-slam: Robust dynamic gaussian splatting slam with hybrid pose optimization,

    Yueming Xu, Haochen Jiang, Zhongyang Xiao, Jianfeng Feng, and Li Zhang, “Dg-slam: Robust dynamic gaussian splatting slam with hybrid pose optimization,” arXiv preprint arXiv:2411.08373 , 2024

    Pith/arXiv arXiv 2024

  16. [16]

    NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU

    Yuhao Zhang, “Ngd-slam: Towards real-time slam for dynamic envi- ronments without gpu,” arXiv preprint arXiv:2405.07392 , 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  17. [17]

    The levenberg-marquardt algorithm,

    Ananth Ranganathan, “The levenberg-marquardt algorithm,” Tutoral on LM algorithm , vol. 11, no. 1, pp. 101–110, 2004

    work page 2004

  18. [18]

    Stochastic gradient descent,

    Nikhil Ketkar and Nikhil Ketkar, “Stochastic gradient descent,” Deep learning with Python: A hands-on introduction , pp. 113–132, 2017

    work page 2017

  19. [19]

    A benchmark for the evaluation of rgb-d slam systems,

    J ¨urgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers, “A benchmark for the evaluation of rgb-d slam systems,” in 2012 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2012, pp. 573–580

    work page 2012

  20. [20]

    Refusion: 3d reconstruction in dynamic environments for rgb-d cameras exploiting residuals,

    Emanuele Palazzolo, Jens Behley, Philipp Lottes, Philippe Giguere, and Cyrill Stachniss, “Refusion: 3d reconstruction in dynamic environments for rgb-d cameras exploiting residuals,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 7855–7862

    work page 2019

  21. [21]

    Nice-slam: Neural implicit scalable encoding for slam,

    Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R Oswald, and Marc Pollefeys, “Nice-slam: Neural implicit scalable encoding for slam,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 12786–12796

    work page 2022

  22. [22]

    Eslam: Efficient dense slam system based on hybrid representation of signed distance fields,

    Mohammad Mahdi Johari, Camilla Carta, and Franc ¸ois Fleuret, “Eslam: Efficient dense slam system based on hybrid representation of signed distance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17408–17419

    work page 2023

  23. [23]

    Gassidy: Gaussian Splatting SLAM in Dynamic Environments

    Long Wen, Shixin Li, Yu Zhang, Yuhong Huang, Jianjie Lin, Fengjunjie Pan, Zhenshan Bing, and Alois Knoll, “Gassidy: Gaussian splatting slam in dynamic environments,” arXiv preprint arXiv:2411.15476 , 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.