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arxiv: 2606.22091 · v1 · pith:IQJBMQ2Gnew · submitted 2026-06-20 · 💻 cs.RO

ACEsplat: Accelerated 3D Gaussian Scene Regression via RGB and Poses Only

Mingkai Liu , Haohua Que , Dikai Fan , Haojia Gao , Tianle Zhu , Handong Yao , Qian Zhang , Ruopeng Zhang
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Xianliang Huang Fei Qiao
This is my paper

Pith reviewed 2026-06-26 11:57 UTC · model grok-4.3

classification 💻 cs.RO
keywords 3D Gaussian Splattingscene reconstructionself-supervised learningnovel view synthesisRGB and poses onlyrobotics mappingreal-time reconstruction
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The pith

ACEsplat reconstructs 3D Gaussian scenes from RGB images and camera poses alone without external geometric priors.

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

The paper introduces a pipeline that builds high-fidelity 3D scene models using only photographs and the camera positions from which they were taken. Standard approaches require separate 3D point clouds or depth maps that take extra time and equipment to produce. The method first runs a self-supervised module that learns scene coordinates directly from the input images and poses to create an internal geometry estimate. This estimate then initializes 3D Gaussian points that are further optimized for rendering. The entire process finishes in 15 to 25 minutes on a single GPU while delivering image quality competitive with methods that use additional 3D data.

Core claim

ACEsplat uses a two-stage pipeline where a self-supervised scene coordinate regression module first builds an internal geometry prior from RGB images and poses in 4-5 minutes, then fuses these with a lightweight Gaussian initialization head for per-scene 3DGS optimization, achieving 29.11 dB PSNR on Wayspots with real-time SLAM poses and 33.20 dB on Cambridge Landmarks with SfM-refined poses while completing the full reconstruction in 15-25 minutes without external 3D priors.

What carries the argument

The two-stage pipeline of self-supervised scene coordinate regression that supplies geometry priors to a lightweight Gaussian initialization head before 3D Gaussian Splatting optimization.

Load-bearing premise

The self-supervised scene coordinate regression produces an internal geometry prior accurate enough to support effective Gaussian initialization and optimization.

What would settle it

Evaluating the full pipeline on a held-out scene collection where the scene coordinate regression outputs show average errors exceeding 10 cm and checking whether final PSNR drops below 25 dB would test the central claim.

Figures

Figures reproduced from arXiv: 2606.22091 by Dikai Fan, Fei Qiao, Handong Yao, Haohua Que, Haojia Gao, Mingkai Liu, Qian Zhang, Ruopeng Zhang, Tianle Zhu, Xianliang Huang.

Figure 1
Figure 1. Figure 1: Overview of the ACEsplat pipeline. Given RGB images and camera poses, ACEsplat first uses an ACE-based self-supervised scene coordinate regression (SCR) module to produce an SCR-derived internal geometry prior (scene coordinates / point cloud) together with image feature descriptors. These geometry priors and features are concatenated and fed into a Gaussian attribute initialization head to initialize 3D G… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative results on the Cambridge Landmarks dataset. ACEsplat achieves strong static-view rendering quality on large-scale scenes using SCR-derived geometric priors and SfM-refined poses [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Runtime vs. rendering quality on Cambridge Landmarks (ACEsplat vs. SfM+3DGS). At every runtime budget ACEsplat attains higher PSNR than the SfM+3DGS pipeline and reaches strong quality within a few minutes of per-scene optimization, avoiding the lengthy SfM feature￾extraction and matching stages. The plotted SfM+3DGS configuration and runtime accounting are detailed in Sec. IV-A. that higher localization a… view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative results on the Wayspots dataset with real￾time SLAM poses. Given single RGB inputs and SLAM poses, ACEsplat reconstructs high-fidelity static views across diverse outdoor scenes. B. Static-View Rendering 1) Wayspots: Rendering with Real-time SLAM Poses: Real-time SLAM poses are often noisy in challenging out￾door environments, which directly affects reprojection-based SCR training and the quali… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison on RealEstate10K (sparse-view novel view synthesis). Given two input views (left), ACEsplat produces compet￾itive novel-view renderings and preserves scene structure more faithfully than several prior sparse-view methods [13], [14], [42] [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: High-resolution novel view synthesis on RealEstate10K. ACEsplat maintains fine details and color fidelity on upscaled scenes (input / ground truth / rendered view). provides a practical RGB+pose-only per-scene alternative with competitive fidelity and short single-GPU adaptation time. Qualitative results in [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablation on geometric priors for sparse-view novel view synthe￾sis. ACE-based SCR priors produce more consistent geometry and higher￾fidelity renderings than monocular-depth-based priors from ZoeDepth [25]. 1) High-resolution scenarios: On upscaled RealEstate10K images at 360 × 640, ACEsplat preserves fine details and color consistency ( [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: On-robot localization demo. ACE/SCR-based localization component successfully estimates camera pose on a wheeled mobile robot with monocular camera. only for evaluation), which discourages view-inconsistent appearance; a systematic study of SH-degree scheduling is left to future work. (ii) SCR failure modes. Because the SCR prior is learned from reprojection, it can be unreliable in textureless or highly r… view at source ↗
read the original abstract

Per-scene 3D Gaussian Splatting (3DGS) enables high-fidelity rendering, but practical robotic and AR scene capture pipelines often depend on external geometric initialization (e.g., SfM point clouds or depth estimates), which can be slow and brittle in on-site deployment. We present ACEsplat, a fast per-scene optimization framework that reconstructs 3D Gaussian representations from RGB images and camera poses only, without requiring external 3D priors (e.g., precomputed SfM models or supervised depth maps). ACEsplat uses a two-stage pipeline: (1) a self-supervised scene coordinate regression (SCR) module builds an internal geometry prior within 4--5 minutes; (2) SCR features and coordinate priors are fused by a lightweight Gaussian initialization head, followed by per-scene 3DGS optimization. On static-view rendering, ACEsplat achieves 29.11 dB PSNR on Wayspots with real-time SLAM poses and 33.20 dB on Cambridge Landmarks with SfM-refined poses. On RealEstate10K sparse-view novel view synthesis, it achieves competitive image fidelity under a challenging 2-view setting. ACEsplat completes scene-specific SCR mapping and 3DGS reconstruction within 15--25 minutes on a single GPU, making it a practical RGB+pose-only solution for rapid scene setup in robotics and mixed-reality applications.

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.

Referee Report

0 major / 2 minor

Summary. The paper presents ACEsplat, a two-stage per-scene optimization framework for 3D Gaussian Splatting (3DGS) reconstruction from RGB images and camera poses only, without external 3D priors. Stage 1 employs a self-supervised scene coordinate regression (SCR) module to build an internal geometry prior in 4-5 minutes. Stage 2 fuses SCR features and coordinate priors via a lightweight Gaussian initialization head, followed by 3DGS optimization. Reported results include 29.11 dB PSNR on Wayspots (real-time SLAM poses), 33.20 dB on Cambridge Landmarks (SfM-refined poses), competitive performance on RealEstate10K under 2-view settings, and total runtime of 15-25 minutes on a single GPU.

Significance. If the central results hold, the work offers a practical advance for robotic and AR scene capture by removing dependence on slow or brittle external geometric initializations such as SfM point clouds or supervised depth. The concrete PSNR and timing numbers on standard benchmarks, combined with the RGB+pose-only constraint, position the method as a potential enabler for rapid on-site deployment.

minor comments (2)
  1. Abstract: the reported PSNR figures (29.11 dB, 33.20 dB) would be strengthened by inclusion of per-scene standard deviations or error bars to convey variability.
  2. The manuscript should provide explicit details on the self-supervised loss terms and training schedule of the SCR module (e.g., in the methods section) to support reproducibility of the geometry prior.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We sincerely thank the referee for their careful review and for recommending minor revision. We appreciate the positive evaluation of the significance of our work on ACEsplat for practical robotic and AR applications. The referee summary accurately describes the method and results. Since the report does not list any major comments, we have no specific rebuttals to provide. We will incorporate any minor comments into the revised manuscript.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents a two-stage pipeline: self-supervised SCR from RGB+poses to build an internal geometry prior, followed by fusion into a Gaussian initialization head and 3DGS optimization. No equations, fitted parameters, or self-citations are quoted that reduce any claimed prediction or result to its inputs by construction. Reported PSNR and timing figures are empirical outcomes on external benchmarks (Wayspots, Cambridge Landmarks, RealEstate10K), not forced equivalences. The central claim remains independent of the input data by the paper's own description.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; it does not expose the internal equations, loss functions, or architectural assumptions of the SCR module or Gaussian head, preventing enumeration of free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5819 in / 1209 out tokens · 36430 ms · 2026-06-26T11:57:42.262506+00:00 · methodology

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