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arxiv: 2604.15612 · v1 · submitted 2026-04-17 · 💻 cs.RO · cs.CV

GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow

Dong-Uk Seo , Jinwoo Jeon , Eungchang Mason Lee , Hyun Myung This is my paper

Pith reviewed 2026-05-10 08:51 UTC · model grok-4.3

classification 💻 cs.RO cs.CV
keywords slamgaussianflowmonocularaccuracycuesflowgaussiangaussians
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The pith

GaussianFlow SLAM aligns projected Gaussian motion with optical flow to regularize monocular 3D Gaussian splatting SLAM, yielding better map quality and pose accuracy than prior methods.

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

Monocular SLAM tries to build 3D maps and track camera movement using only one camera, but it often struggles because a single image lacks direct depth information. This can cause the map to have wrong shapes or the camera position estimates to drift. Gaussian splatting represents scenes as many small 3D blobs that can render photo-realistic views, but without extra cues it can settle into bad solutions. The new method computes how those blobs would move when the camera moves, calls this GaussianFlow, and forces it to match the optical flow seen in the video. This adds a geometry signal that helps both the map and the poses stay consistent. They also add rules to add or remove blobs based on error measures to keep the map clean. Tests on standard datasets show better pictures and more accurate tracking than other recent systems.

Core claim

Experiments conducted on public datasets demonstrate that our method achieves superior rendering quality and tracking accuracy compared with state-of-the-art algorithms.

Load-bearing premise

That aligning the projected motion of Gaussians (GaussianFlow) with optical flow provides consistent structural cues sufficient to regularize both map reconstruction and pose estimation and avoid local minima in monocular settings.

Figures

Figures reproduced from arXiv: 2604.15612 by Dong-Uk Seo, Eungchang Mason Lee, Hyun Myung, Jinwoo Jeon.

Figure 1
Figure 1. Figure 1: Illustration of GaussianFlow SLAM process. Our method leverages [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) The overall framework of GaussianFlow SLAM. Each of the three modules highlighted by the dashed box performs a recurrent update process [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of per-Gaussian error types for the rendered image from [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of rendered images and rasterized depths for a challenging [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison on the EuRoC dataset. By exploiting optical flow for geometric learning, our approach preserves fine object boundaries and [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Gaussian splatting has recently gained traction as a compelling map representation for SLAM systems, enabling dense and photo-realistic scene modeling. However, its application to monocular SLAM remains challenging due to the lack of reliable geometric cues from monocular input. Without geometric supervision, mapping or tracking could fall in local-minima, resulting in structural degeneracies and inaccuracies. To address this challenge, we propose GaussianFlow SLAM, a monocular 3DGS-SLAM that leverages optical flow as a geometry-aware cue to guide the optimization of both the scene structure and camera poses. By encouraging the projected motion of Gaussians, termed GaussianFlow, to align with the optical flow, our method introduces consistent structural cues to regularize both map reconstruction and pose estimation. Furthermore, we introduce normalized error-based densification and pruning modules to refine inactive and unstable Gaussians, thereby contributing to improved map quality and pose accuracy. Experiments conducted on public datasets demonstrate that our method achieves superior rendering quality and tracking accuracy compared with state-of-the-art algorithms. The source code is available at: https://github.com/url-kaist/gaussianflow-slam.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The approach rests on standard assumptions from Gaussian splatting and optical flow literature plus two new modules whose internal parameters are not detailed in the abstract; no explicit free parameters, axioms, or invented entities beyond the term GaussianFlow are visible.

invented entities (1)
  • GaussianFlow no independent evidence
    purpose: Projected motion of Gaussians used as a geometry-aware cue to align with optical flow
    Term coined in the paper to represent the motion signal that regularizes optimization; no independent evidence outside the method is provided.

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Reference graph

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