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REVIEW 4 major objections 5 minor 1 cited by

ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper's central claim is that Gaussian-splatting surface extraction scales to ultra-large aerial scenes when sub-regions are partitioned by SfM points and trained with multi-view depth and normal consistency.

desk verdict Solid large-scale GS surface-reconstruction system whose F1 claims are undercut by per-method evaluation cropping; worth reviewing after the metric protocol is fixed. read the letter →

arxiv 2412.01402 v3 pith:HDXYZGNO submitted 2024-12-02 cs.CV

Zhuoxiao Li , Shanliang Yao , Taoyu Wu , Yong Yue , Wufan Zhao , Rongjun Qin , Angel F. Garcia-Fernandez , Andrew Levers
show 1 more author
Xiaohui Zhu
This is my paper · ORCID
classification cs.CV
keywords GaussianSplattingSurfaceReconstructionLarge-ScaleScenesAerialPhotogrammetryUrbanSceneMulti-ViewGeometricConsistencyPointCloudPartitioning
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

The paper argues that Gaussian Splatting, which already gives fast rendering and small-scene surface extraction, can be pushed to ultra-large-scale aerial photogrammetry if the scene is partitioned the right way. ULSR-GS divides the scene using the sparse SfM point cloud instead of camera positions, then assigns each 3D point the four-image group that sees it closest to the image centers, so every sub-region gets the most informative training views. During training it densifies with multi-view depth aggregation and adds depth- and normal-consistency losses, which preserve fine surface detail that single-view densification washes out. The method extracts each sub-region's mesh independently and stitches them without global fusion, avoiding the memory blow-up of merging the whole scene. On the GauU-Scene, Matrix City, and two custom aerial benchmarks, the paper reports higher F1 than existing GS-based extractors, and comparable F1 to MVS pipelines at a fraction of the runtime.

What carries the argument

The machinery has three coupled parts. First, the point-to-photo partition: after density filtering of the SfM cloud, the scene is split into rectangular sub-regions, and for every point the method picks the group of one reference plus three source views whose projections lie closest to the image centers, discarding images that no point selects. Second, adaptive multi-view densification: rendered depths from the three source views are fused with geometric-consistency weights and reprojected as new 2D Gaussian primitives only inside an adaptive window mask whose size shrinks where the depth gradient is high. Third, two consistency losses, a depth reprojection error and a normal-vector angular error between reference and source views, push the trained surface to agree across views. Per-region TSDF fusion with boundary cropping then extracts meshes that stitch together without a global fusion step.

What would settle it

Re-run the same scene evaluations with the LiDAR ground truth fixed by surveyed ground-control markers instead of best-fit alignment, or perturb the alignment by a few centimeters and recompute F1; if the reported margins over PGSR and CityGaussianV2 shrink below the perturbation or reverse, the accuracy claim would be measuring alignment quality rather than reconstruction quality.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the bottleneck in large-scale Gaussian surface reconstruction is not the splatting itself but the way the scene is split and supervised. ULSR-GS shows that a point-to-photo partition, in which each SfM point selects its most central four-view group, produces sub-regions dense and consistent enough for 2D Gaussian Splatting to recover fine urban geometry locally, and that the local meshes tile into a coherent whole without merging. Multi-view depth and normal consistency constraints are what carry the improvement: in the ablation, removing both drops F1 from 0.685 to 0.660, while removing only the normal term gives 0.651 and removing only the depth term gives 0.664. The paper's headline numbers include F1 0.718 on SZTU versus 0.672 for PGSR, F1 0.892 on Matrix City versus 0.886 for CityGaussianV2, and competitive F1 with COLMAP on Scene 1 while taking roughly a third of the runtime.

Load-bearing premise

The headline accuracy numbers assume the laser-scanned ground truth and the reconstructed mesh can be aligned well enough by a best-fit geometric alignment after cropping to their overlap, and that the resulting precision, recall, and F1 therefore measure surface fidelity rather than registration error.

Editorial extensions

If this is right

  • GS-based mesh extraction can scale to scenes with thousands of oblique aerial images by training sub-regions independently on multiple GPUs, without building a whole-scene dense point cloud or mesh.
  • View selection should be driven by the geometry being reconstructed from SfM points and their optimal image groups, not by camera positions; this matters most for irregular and close-range flight paths.
  • Multi-view depth and normal consistency, not densification alone, are the main levers for surface fidelity in large urban scenes; the normal-consistency term is the more influential of the two in the ablation.
  • Compared to MVS, a GS pipeline can deliver comparable precision and recall on city-scale meshes in about a third of the time, for instance Scene 1 at F1 0.727 in 19.8 hours versus COLMAP at 0.756 in 69.2 hours.
  • Sub-region TSDF extraction followed by direct stitching is sufficient to avoid seams at sub-region boundaries, provided each region starts with doubled boundaries and redundant points are pruned by opacity culling.

Reading between the lines

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

  • One consequence the paper leaves implicit is that its per-point optimal view selection is a general principle: any reconstruction pipeline that starts from SfM tracks could adopt the same closest-to-principal-point four-view group rule to improve its training-view selection.
  • The ablation pattern hints that densification is not the primary source of geometric accuracy; if so, pairing the consistency losses with cheaper or learned densification might recover most of the benefit at lower memory cost, which the paper does not test.
  • The method's stated limit near 1.6K rendering resolution and its weakness on vegetation suggest the next testable extension is a higher-resolution rasterizer plus a semantic or depth-prior branch for foliage, which the paper lists as future work.
  • The evaluation's reliance on best-fit alignment after bounding-box cropping means the reported F1 gaps should be re-checked with survey-registered ground truth; that check is not in the paper but is directly enabled by the evaluation protocol it describes.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes ULSR-GS, a large-scale surface reconstruction framework built on 2D Gaussian Splatting. Its three main components are a point-to-photo scene partitioning strategy driven by sparse SfM points, a per-point optimal view selection that follows the MVS-Net scoring formulation, and an adaptive multi-view geometric-consistency densification with depth and normal consistency losses. The method is evaluated on the GauU-Scene benchmark, Matrix City, and two custom-collected aerial datasets, comparing against single-GPU GS baselines (SuGaR, 2DGS, GOF, PGSR), a multi-GPU baseline (CityGaussianV2), and MVS pipelines (COLMAP, Reality Capture), using precision, recall, and F1 against LiDAR ground truth. The central claim is that ULSR-GS outperforms existing GS-based surface extraction on large-scale aerial photogrammetry while being more time-efficient than MVS.

Significance. If the reported results are valid, ULSR-GS is a practically relevant contribution to city-scale photogrammetry: it extracts meshes per sub-region without merging the entire scene first, and its ablation table is consistent with the individual contributions of the proposed modules (removing normal consistency lowers F1 from 0.685 to 0.651, removing densification lowers it to 0.682). Strengths include evaluation against external LiDAR on six GauU scenes, a multi-GPU pipeline with per-tile training, and a clear qualitative comparison. The main quantitative claim, however, rests on an evaluation protocol that crops each mesh to its own overlap with LiDAR; because ULSR-GS deliberately discards sparse background sub-regions, this protocol can reward selective reconstruction and confound cross-method comparisons. The headline results are therefore not yet fully supported.

major comments (4)
  1. [Sec. 5.3.2, Tables 2-4] The mesh evaluation protocol crops both the reconstructed mesh and the LiDAR ground truth to their overlapping axis-aligned bounding box before any metric is computed. Since each method has a different mesh extent, precision, recall, and F1 are computed on different domains for different methods. Sections 4.1.5 and Table 7 explicitly state that ULSR-GS discards sparse background sub-regions and that the partition strategy 'deletes the background area.' Under the overlap-cropping protocol, the omitted background LiDAR points are removed from the recall denominator for ULSR-GS, and any out-of-bbox mesh geometry is removed from the precision numerator. This makes the F1 rankings in Tables 2-4 reflect evaluation extent as well as geometric accuracy. The authors should recompute all metrics on a fixed evaluation domain (e.g., the full LiDAR bounding box or the union of all compared mesh bounding boxes), report the fraction of LiDAR points inside each mesh's overlap, and report ICP residuals and the sensitivity of F1 to the alignment threshold. Without this, the central comparison claim is not established.
  2. [Sec. 5.5.1, Table 2] The comparison with single-GPU baselines is not hardware-matched. Ours trains each sub-region on one of four RTX 4090 GPUs, while SuGaR, 2DGS, GOF, and PGSR run on a single GPU at 60k iterations. The wall-clock time column in Table 2 for Ours therefore does not reflect the roughly four times higher GPU-hour consumption. The claimed efficiency advantage over GOF and PGSR should be expressed in GPU-hours, with the iteration budget and training resolution per sub-region specified. If the intended claim is quality at comparable compute, the baselines should be run under a comparable multi-GPU setting, or the text should restrict the claim to end-to-end wall-clock time on the authors' specific hardware configuration.
  3. [Sec. 4.2.2, Eq. (18)] The adaptive densification window size is defined as (h_win, w_win) = k / (g_bar + epsilon) * (h, w) / 2, but the values of k and epsilon are never reported in Section 5.1 or in the ablations. Since the window mask is essential to avoid GPU out-of-memory (as shown by the ablation 'w densify & w/o window mask' in Table 5) and directly controls the number of added Gaussians, the choice of k and epsilon materially affects the densification outcome. Please report the values used and, if possible, provide a sensitivity analysis.
  4. [Sec. 5.3.2, Table 2] The threshold 'tau = 0.025 (relative)' is not defined. The Tanks and Temples pipeline normally uses absolute thresholds in scene units; the paper should state how 'relative' is computed per scene (e.g., as a fraction of the bounding-box diagonal or mean scene scale). Without this, the F1 numbers in Table 2 are not reproducible, and it is unclear whether the same physical tolerance is used across the six GauU-Scene sites.
minor comments (5)
  1. [Sec. 4.1.4] There is a typo 'we we calculate' in the description of the average Euclidean distance, and 'agrmin' in Eq. (12) should be 'argmin'.
  2. [Figure captions] Several figure captions contain typos, including 'cumtom' for 'custom' in Figures 8-9, 'ars' for 'are' in Figures 14-15, and 'Rensults' for 'Results' in Figure 5.
  3. [References] The same paper appears twice in the reference list: VastGaussian is listed as [29] and again as [30] with identical metadata; the in-text citations should be unified.
  4. [Sec. 4.3, Eq. (21)] The symbols L_geo and L_r in the final loss are not explicitly defined; the text later describes them as the 2DGS and 3DGS losses, but the equation should carry a clear definition.
  5. [Sec. 5.3.2] The sentence 'We uniformly sample number of points' is missing an article; it should read 'We uniformly sample the number of points'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central reconstruction claims are evaluated against external LiDAR ground truth, and the proposed modules are independently ablated.

full rationale

ULSR-GS's headline claims are quantitative comparisons of extracted meshes against external LiDAR ground truth (Sec. 5.3.2, Tables 2-4), not predictions derived from fitted parameters. The view-selection score in Eqs. (1)-(2) is taken from the published MVSNet formulation, and the multi-view depth and normal losses in Eqs. (14)-(21) are stated optimization objectives, not hidden re-statements of the evaluation metric. The ablations in Tables 5 and 6 vary the proposed consistency losses, densification, and partitioning strategy and show measurable F1 differences, so the contributions are not equivalent to their inputs by construction. The only self-citation is to the authors' prior MVG-Splatting [27] for the rescaling and rotational alignment of newly added Gaussians (Sec. 4.2.2); this is an implementation detail and is not the load-bearing justification for any central claim, nor is it invoked as a uniqueness theorem. One evaluation-extent concern is explicitly flagged by the paper itself: Table 7 admits that the partition 'deletes the background area,' while Sec. 5.3.2 computes metrics after cropping both mesh and LiDAR to their overlapping bounding box; this could make cross-method F1 comparisons sensitive to per-method mesh extent. That is a benchmark-fairness or validation risk, not circularity, because the comparison still uses external GT geometry and no metric value is defined by the method's own outputs. Accordingly, no circular step meets the quoted-evidence bar, and the appropriate score is 0.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The central claim rests on roughly five hand-set hyperparameters (view-selection Gaussian parameters, window scaling constants, depth-weighting sigma, loss weights), none of which are ablated or given numeric values in the text. The other assumptions are standard domain assumptions for any GS+TSDF pipeline: COLMAP SfM correctness and ICP-based evaluation alignment. No new physical or mathematical entities are postulated.

free parameters (5)
  • theta_0 (preferred baseline angle) = not stated in text
    The piecewise Gaussian in Eq. 2 needs theta_0, sigma_1, sigma_2 values for the multi-view pair selection in Eq. 8. The paper says it follows the MVS-Net setting [61] but does not give the numeric value.
  • k and epsilon in Eq. 18 (adaptive window size) = not stated
    The window (h_win, w_win) = k/(g_bar + epsilon) * (h,w)/2 depends on two constants k and epsilon, which are described as 'proportional constants' without numeric values or ablation.
  • sigma_2 in Eq. 15 (depth confidence weighting) = not stated
    The geometric consistency weight w = exp(-E_depth / sigma^2) uses a variance sigma^2 that is not specified in the text.
  • epsilon_error threshold for SfM reprojection error = >1.5
    The point cloud cleaning threshold is given only as epsilon_error > 1.5, a hand-chosen value with no sensitivity analysis.
  • alpha=0.01 and beta=0.1 loss weights = alpha=0.01, beta=0.1
    The weights of the depth and normal consistency losses in Eq. 21 are set without ablation or sensitivity analysis.
assumptions (3)
  • domain assumption SfM sparse point cloud and camera poses from COLMAP are accurate enough to drive the partitioning and view selection.
    The entire partitioning (Sec. 4.1) and per-point view selection depend on the correctness of the COLMAP sparse reconstruction; the paper does not report reprojection error statistics or validation of the sparse points.
  • domain assumption The Tanks-and-Temples-style ICP alignment and bounding-box cropping are sufficient to align mesh and LiDAR ground truth before computing precision/recall.
    Sec. 5.3.2 assumes ICP after cropping yields a valid comparison; no alignment error or sensitivity analysis is reported.
  • domain assumption 2DGS depth rendering (Eq. 3, D_mean) provides a geometrically meaningful depth map for the multi-view consistency and densification.
    Sec. 3.2 states D_mean is adopted as the depth representation. The fidelity of the final mesh depends on this depth approximation and on TSDF fusion of these depths.

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Cite this review

Pith. "Pith review of ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency." pith.science (2026). https://pith.science/paper/HDXYZGNO

@misc{pith2026241201402,
  author       = {Pith},
  title        = {Pith review of: ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HDXYZGNO}},
  note         = {Machine review of arXiv:2412.01402}
}
read the original abstract

While Gaussian Splatting (GS) demonstrates efficient and high-quality scene rendering and small area surface extraction ability, it falls short in handling large-scale aerial image surface extraction tasks. To overcome this, we present ULSR-GS, a framework dedicated to high-fidelity surface extraction in ultra-large-scale scenes, addressing the limitations of existing GS-based mesh extraction methods. Specifically, we propose a point-to-photo partitioning approach combined with a multi-view optimal view matching principle to select the best training images for each sub-region. Additionally, during training, ULSR-GS employs a densification strategy based on multi-view geometric consistency to enhance surface extraction details. Experimental results demonstrate that ULSR-GS outperforms other state-of-the-art GS-based works on large-scale aerial photogrammetry benchmark datasets, significantly improving surface extraction accuracy in complex urban environments. Project page: https://ulsrgs.github.io.

Figures

Figures reproduced from arXiv: 2412.01402 by the authors.

Figure 1
Figure 1. ULSR-GS training pipeline. Stage 1: Scene partitioning based on point cloud. Stage 2: Training strategy based on multi-view geometric consistency. Stage 3: Extract the geometry of sub-regions separately and seamlessly merge. select views for the partitioned sub-regions. Different from previous studies that use drone photo locations for region partitioning [29, 67, 33], our scene partitioning method is based on the i… view at source ↗
Figure 2
Figure 2. Point-based partition strategy of ULSR-GS. (I) We partition the training scene based on the positions of the point cloud projected onto the xz-plane. (II) For each sub-scene: (a) we first select all matching images within the region; (b) based on the camera relationships of the sub-scene, we select the corresponding images for each point; (c) following the principle of multi-view optimal matching [61], we filter out… view at source ↗
Figure 3
Figure 3. Examples of experiment datasets. Our experiments are conducted on different types of aerial oblique photography datasets, such as (a) close-range photogrammetry, (b) five￾directional (penta-cam) photogrammetry, and (c) hybrid pho￾togrammetry which contains both close-range and penta-cam photogrammetry. Z.X. Li et al.: Preprint Page 15 of 25 [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison with single GPU GS-based methods. We show the full scene comparison with 2DGS [16], GOF[66], and PGSR[5]. From left to right are the LFLS, SZIIT, and SMBU scenes of the GauU-Scene [54] dataset [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: Rendering Rensults. We demonstrate the full-scene (zoomed-out) rendering detail of the selected scenes. Z.X. Li et al.: Preprint Page 16 of 25 [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: Qualitative comparison on the GauU-Scene dataset [54]. We demonstrate the zoomed-in surface normal detail of the selected areas in red box [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Qualitative comparison on the GauU-Scene [35]dataset SZTU scene. When the scene range expands and the number of images increases, the geometric reconstruction quality of 2DGS [16] and PGSR [5] drops sharply. Z.X. Li et al.: Preprint Page 17 of 25 [PITH_FULL_IMAGE:figu…
Figure 8
Figure 8. Figure 8: Qualitative comparison between ULSR-GS and CityGaussianV2 [34] on the Matrix City dataset [25] Small City scene and the cumtom collected dataset Scene1. Our method is superior to CityGaussianV2 [34] in terms of building integrity and detail preservation. Z.X. Li et al.…
Figure 9
Figure 9. Figure 9: Qualitative comparison between ULSR-GS and Reality Capture and COLMAP on the cumtom collected dataset Scene1. We use a image downsampling factor of 4× on every method, and use the highest setting of Reality Capture and COLMAP for fair comparison. It can be seen that wi…
Figure 10
Figure 10. Figure 10: Qualitative evaluation of the Recall metric and the generated DSM. Z.X. Li et al.: Preprint Page 19 of 25 [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]
Figure 11
Figure 11. Figure 11: Qualitative evaluation of different parameter settings. From left to right: GT LiDAR, ULSR-GS with TSDF-fusion at 4x downsampling and 0.4m voxel size, ULSR-GS with TSDF-fusion at 4x downsampling and 0.1m voxel size, CityGaussianV2 with TSDF-fusion at 4x downsampling a…
Figure 12
Figure 12. Figure 12: Qualitative comparison on the geometric consis￾tency constraints ablation studies. Without 𝐸𝑑𝑒𝑝𝑡ℎ & 𝐸𝑛𝑜𝑟𝑚𝑎𝑙 edges of each region fail to stitch seamlessly; Without adaptive densify results in excessively smooth mesh extraction out￾comes. Z.X. Li et al.: Preprint Page …
Figure 13
Figure 13. Figure 13: Qualitative comparison on the adapted depth densification ablation studies. Without adaptive densification (e.g. vanilla 2dgs greadient-based densification) results in excessively smooth mesh extraction outcomes and loss rendering quality [PITH_FULL_IMAGE:figures/ful…
Figure 14
Figure 14. Figure 14: Qualitative comparison of partition strategies on a penta-cam dataset. The black bounding box is the same partition area. The image partition boundary for VastGaussian and and the point cloud partition boundary for ULSR-GS ars set to the same size according to the bla…
Figure 15
Figure 15. Figure 15: Qualitative comparison of partition strategies on a close-range dataset. The black bounding box is the same partition area. The image partition boundary for VastGaussian and and the point cloud partition boundary for ULSR-GS ars set to the same size according to the b…
Figure 16
Figure 16. Figure 16: Qualitative comparison of partition strategies. In close-range photography tasks, a large number of background areas will appear. Our partitioning method emphasizes high-quality rendering of the reconstructed subject and ignores the division of background areas. Z.X. …
Figure 17
Figure 17. Figure 17: Experimental Location. (a) Bird-eye-view of the experimental location Moon Bay, Dushu Lake, Suzhou. (b) USV and its SBES mathymetry footprints. (c) UAV and its oblique photogrammetry footprints [PITH_FULL_IMAGE:figures/full_fig_p024_17.png]
Figure 18
Figure 18. Figure 18: Zoomed-in detail of the fusion USV and UAV models. Left: Mesh; Right Normal. Z.X. Li et al.: Preprint Page 24 of 25 [PITH_FULL_IMAGE:figures/full_fig_p024_18.png]
Figure 19
Figure 19. Figure 19: Mesh Results on GauU-Scene [54] and UrbanScene 3D [35] Datasets. Z.X. Li et al.: Preprint Page 25 of 25 [PITH_FULL_IMAGE:figures/full_fig_p025_19.png]
Figure 20
Figure 20. Figure 20: Mesh Results on MatrixCity [25] Dataset. Z.X. Li et al.: Preprint Page 26 of 25 [PITH_FULL_IMAGE:figures/full_fig_p026_20.png]
Figure 21
Figure 21. Figure 21: Mesh Results on Scene 1. Z.X. Li et al.: Preprint Page 27 of 25 [PITH_FULL_IMAGE:figures/full_fig_p027_21.png]
Figure 22
Figure 22. Figure 22: Mesh Results on Scene 2. Z.X. Li et al.: Preprint Page 28 of 25 [PITH_FULL_IMAGE:figures/full_fig_p028_22.png]

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