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arxiv: 2606.01419 · v1 · pith:36VKLQYKnew · submitted 2026-05-31 · 💻 cs.CV

DENSER: Depth-Guided Ensemble with Staged EFA-GS Reconstruction for Soccer Novel View Synthesis

Parthsarthi Rawat This is my paper

Pith reviewed 2026-06-28 17:06 UTC · model grok-4.3

classification 💻 cs.CV
keywords novel view synthesisGaussian splattingdepth supervisionensemblesoccer videocamera calibration3D reconstruction
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The pith

DENSER extends EFA-GS for soccer novel view synthesis by adding camera-height loss weighting, monocular depth supervision, and a three-model ensemble.

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

The paper proposes DENSER to improve novel view synthesis specifically for soccer footage. It modifies an existing reconstruction method through three targeted changes that prioritize typical broadcast camera positions, add geometric guidance where images lack texture, and average results from models trained with slight differences. These steps produce reported quality scores of 29.89 dB PSNR, 0.791 SSIM, and 0.366 LPIPS on five held-out scenes. A reader would care if the changes translate into more accurate new viewpoints for sports analysis or replay without requiring additional camera hardware.

Core claim

DENSER extends EFA-GS with camera-height-based loss weighting that prioritises ground-level broadcast views, monocular depth supervision from Depth-Anything-V2 to regularise geometry in textureless regions, and a three-model pixel-average ensemble whose members diverge from a shared base checkpoint by varying training length and Gaussian scale clamping. On five held-out challenge scenes we achieve a mean PSNR of 29.89 dB, SSIM of 0.791, and LPIPS of 0.366.

What carries the argument

The depth-guided ensemble with staged EFA-GS reconstruction, where camera-height loss weighting and monocular depth estimates regularize the scene geometry before pixel averaging across models.

If this is right

  • Ground-level views receive stronger influence during optimization, aligning the reconstruction with common broadcast camera placements.
  • Textureless areas such as field markings gain geometric constraints that reduce floating artifacts in synthesized frames.
  • Averaging three models trained from the same checkpoint but with different lengths and scale clamps lowers variance in the final output.
  • Staged reconstruction allows progressive refinement of both appearance and geometry without restarting from scratch.

Where Pith is reading between the lines

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

  • The same height-weighting and depth terms could be tested on other multi-camera sports datasets to check transfer beyond soccer.
  • Replacing the specific monocular depth model with alternatives would reveal how much the final metrics depend on the choice of depth estimator.
  • The ensemble averaging step might be replaced by a single longer training run with scheduled scale clamping to measure whether multiple models are strictly necessary.

Load-bearing premise

Monocular depth estimates provide reliable regularization of scene geometry in textureless regions.

What would settle it

Running the base EFA-GS model versus the full DENSER pipeline on the same five held-out scenes and checking whether PSNR, SSIM, and LPIPS improve only when the depth supervision term is active.

Figures

Figures reproduced from arXiv: 2606.01419 by Parthsarthi Rawat.

Figure 1
Figure 1. Figure 1: Qualitative comparison on Scene 3, camera 22. 3DGS exhibits a pervasive green colour cast and loses fine structure on the goal [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
read the original abstract

We propose DENSER, a Depth-guided ENSemble with Staged EFA-GS Reconstruction for soccer novel view synthesis. DENSER extends EFA-GS with three key contributions: (1) camera-height-based loss weighting that prioritises ground-level broadcast views, (2) monocular depth supervision from Depth-Anything-V2 to regularise geometry in textureless regions, and (3) a three-model pixel-average ensemble whose members diverge from a shared base checkpoint by varying training length and Gaussian scale clamping. On five held-out challenge scenes we achieve a mean PSNR of 29.89 dB, SSIM of 0.791, and LPIPS of 0.366.

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

1 major / 1 minor

Summary. The manuscript proposes DENSER, an extension of EFA-GS for soccer novel view synthesis. It adds three contributions: (1) camera-height-based loss weighting prioritizing ground-level views, (2) monocular depth supervision from Depth-Anything-V2 to regularize geometry in textureless regions, and (3) a three-model pixel-average ensemble with members diverging via training length and Gaussian scale clamping. On five held-out challenge scenes, it reports mean PSNR 29.89 dB, SSIM 0.791, and LPIPS 0.366.

Significance. If the depth supervision and ensemble claims are substantiated with ablations and baselines, the work could provide a practical engineering advance for geometry regularization in broadcast soccer NVS, where textureless pitch regions and varying camera heights are common. The staged reconstruction and ensemble approach are straightforward extensions that could be adopted if shown to be robust.

major comments (1)
  1. [Abstract, contribution (2)] Abstract, contribution (2): the claim that monocular depth supervision from Depth-Anything-V2 regularizes geometry where RGB losses are weak is load-bearing for the reported metrics, yet no depth-error quantification, GT comparison on soccer scenes, or ablation isolating this term is provided. If Depth-Anything-V2 exhibits systematic bias or high variance on uniform pitches and moving players, the geometry improvements and final PSNR/SSIM/LPIPS values cannot be attributed to this component.
minor comments (1)
  1. [Abstract] Abstract: no baselines, ablation studies, error bars, or dataset details are supplied, preventing verification that the stated metrics exceed prior EFA-GS results or other methods on the same five scenes.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the editor and the referee for the constructive feedback on our manuscript. We address the major comment below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract, contribution (2)] Abstract, contribution (2): the claim that monocular depth supervision from Depth-Anything-V2 regularizes geometry where RGB losses are weak is load-bearing for the reported metrics, yet no depth-error quantification, GT comparison on soccer scenes, or ablation isolating this term is provided. If Depth-Anything-V2 exhibits systematic bias or high variance on uniform pitches and moving players, the geometry improvements and final PSNR/SSIM/LPIPS values cannot be attributed to this component.

    Authors: We agree that an ablation isolating the monocular depth supervision term is necessary to substantiate its contribution. In the revised manuscript we will add a controlled ablation that trains identical base models with and without the Depth-Anything-V2 depth loss on the same five held-out scenes and reports the resulting differences in PSNR, SSIM and LPIPS. This will directly quantify the performance impact of the term. We note, however, that the soccer broadcast dataset provides no ground-truth depth maps, so quantitative depth-error metrics or direct GT comparisons are not feasible; we will instead supply qualitative depth-map visualizations from novel views (with and without the supervision) to demonstrate regularization in textureless regions. These additions will allow readers to assess whether Depth-Anything-V2 introduces systematic bias on pitches or players. revision: yes

standing simulated objections not resolved
  • Ground-truth depth maps are unavailable in the soccer scenes, preventing any quantitative depth-error quantification or GT comparison.

Circularity Check

0 steps flagged

No circularity: empirical extension evaluated on held-out data

full rationale

The paper describes an engineering extension of prior EFA-GS work via camera-height loss weighting, Depth-Anything-V2 depth supervision, and a three-model ensemble, with results reported on five held-out challenge scenes. No equations, derivations, or self-citation chains are present that reduce any claimed prediction or result to fitted inputs by construction. The central metrics (PSNR 29.89 dB etc.) are obtained from external test scenes and an off-the-shelf depth model, so the derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are described. The method implicitly relies on the accuracy of an external depth model and standard Gaussian splatting assumptions.

pith-pipeline@v0.9.1-grok · 5649 in / 1290 out tokens · 33730 ms · 2026-06-28T17:06:47.753864+00:00 · methodology

discussion (0)

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Reference graph

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7 extracted references · 1 canonical work pages · 1 internal anchor

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