G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior
Authors: Junfeng Ni
Yixin Chen
Zhifei Yang
Yu Liu
Ruijie Lu
Song-Chun Zhu
Siyuan Huang
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Scene Reconstruction
Sparse View Reconstruction
Generative Prior
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: G4Splat integrates accurate geometry guidance with generative prior to enhance 3D scene reconstruction, substantially improving both geometric fidelity and appearance quality in observed and unobserved regions.

Abstract: Despite recent advances in leveraging generative prior from pre-trained diffusion models for 3D scene reconstruction, existing methods still face two critical limitations. First, due to the lack of reliable geometric supervision, they struggle to produce high-quality reconstructions even in observed regions, let alone in unobserved areas. Second, they lack effective mechanisms to mitigate multi-view inconsistencies in the generated images, leading to severe shape–appearance ambiguities and degraded scene geometry.
In this paper, we identify accurate geometry as the fundamental prerequisite for effectively exploiting generative models to enhance 3D scene reconstruction. 
We first propose to leverage the prevalence of planar structures to derive accurate metric-scale depth maps, providing reliable supervision in both observed and unobserved regions. 
Furthermore, we incorporate this geometry guidance throughout the generative pipeline to improve visibility mask estimation, guide novel view selection, and enhance multi-view consistency when inpainting with video diffusion models, resulting in accurate and consistent scene completion.
Extensive experiments on Replica, ScanNet++, DeepBlending and Mip-NeRF 360 show that our method consistently outperforms existing baselines in both geometry and appearance reconstruction, particularly for unobserved regions.
Moreover, our method naturally supports single-view inputs and unposed videos, with strong generalizability in both indoor and outdoor scenarios with practical real-world applicability. Project page: https://dali-jack.github.io/g4splat-web/.

Published as a conference paper at ICLR 2026
G4SPLAT: GEOMETRY-GUIDED GAUSSIAN SPLAT- TING WITH GENERATIVE PRIOR
Junfeng 2 Vixin Chen
Zhifei Yang"
Yu Liu
Ruijie Lu
Song-Chun Zhu 1,3
Siyuan Huang 2.
"Work done as an intern at BIGAI
Project lead
Corresponding author
'Tsinghua University
State Key Laboratory of General Artificial Intelligence, BIGAI Peeking University
Comparison with baselines
2DGS
Difix3D+
GuidedVD
Ours
Any Wiewweeewwwee resonstruction
1 input view
5 input views
Figure 1: We propose G4SPLAT, which integrates accurate geometry guidance with generative prior to enhance 3D scene reconstruction. Our method significantly improves geometry and appearance reconstruction quality in both observed and unobserved regions (black areas in baselines), and gen- eralizes well across diverse scenarios, including single-view images and unposed videos.
ABSTRACT
Despite recent advances in leveraging generative prior from pre-trained diffu- sion models for 3D scene reconstruction, existing methods still face two critical limitations. First, due the lack of reliable geometric supervision, they strug- gle to produce high-quality reconstructions even in observed regions, let alone unobserved areas. Second, they lack effective mechanisms to mitigate multi- view inconsistencies in the generated images, leading to severe shape-appeane appearance ambiguities and degraded scene geometry. In this paper, we identify accurate geometry as the fundamental prerequisite for effectively exploiting generative models to enhance 3D scene reconstruction. We first propose to leverage the prevalence of planar structures to derive accurate metric-scale depth maps, pro- viding reliable supervision in both observed and unobserved regions. Further- more, incorporate this geometry guidance throughout the generative pipeline to improve visibility mask estimation, guide novel view selection, and enhance multi-view consistency when inpainting with video diffusion models, resulting in accurate and consistent scene completion. Extensive experiments on Replica, ScanNet++. DeepBlending and Mip-NeRF 360 show that our method consistently outperforms existing baselines in both geometry and appearance reconstruction. particularly for unobserved regions. Moreover, our method naturally supports single-view view inputs and unposed videos, with strong generalizability in both in- door and outdoor scenarios with practical real-world applicability. Project page:
htops: //da1 jack, github. o0/ aappppatteeiea

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recent advances in 3D Gaussain Splatting (3DGS) (Kerbl al., 2023 have significantly improved photo-realistic novel view synthesis by enabling efficient 3D scenes reconstruction with dense view supervision. However, their performance degrades notably in sparsely captured settings due to insuf- ficient geometric and photometric supervision. Depth regularization has been introduced to alleviate this issue (Turkulainen al. 2025 Li et al. 2024a), but faithful reconstruction remains challenging because of the scale ambiguity inherent in monocular depth estimators (Yang et al. [0024). In addi- tion, these methods are ineffective in ander-constrained areas (i.e., regions distant from or invisible to the input views), where no additional scene information can be provided.
More recent approaches (Liu et al., 2024d| Bao et al. 2025) leverage generative knowledge from pre-trained diffusion models (Blattmann et al. 2023 Yu et al. 2024b; Ma et al. 2025) to generate missing content in constrinnd regions. However, these approaches still struggle to faithfully reconstruct unobserved regions while maintaining consistency in the observed areas, as illustrated in Fig. [ We identify two main factors underlying these problems. First, lacking reliable geomet- ric supervision, these methods produce poor reconstruction quality even in observed regions with sparse input views, which undermines the geometric basis essential for inpainting unobserved ar- eas. Second, these methods lack effective mechanisms to mitigate multi-view inconsistencies in diffusion model outputs, which lead to degraded scene recovery due to severe shape-appearance ambiguities (Zhang al. 2020 Zhong et al. (025)).
In this paper, we argue that robust geometry guidance is fundamental to effectively employing the power of generative models and realizing high-quality reconstruction. To this end, we introduce G4SPLAT, which first leverages the prevalence of planar structures in man-made environments, consistent with the Manhattan world assumption (Coughlan Yuille 1999), to derive scale-accurate geometric constraints. Unlike matching-basee methods (Wang al., 2024a| Guédon et al.) 2025) that often fail in non-overlapping regions, planar surfaces allow depth extrapolation: a 3D plane can be reliably estimated from partial depth observations and then extended across the entire surface. We leverage this property by aligning global 3D planes to obtain accluraccurate depth in planar regions and linearly adjusting monocular depth for unobserved non- non-planar regions, resulting in plane-awan depth maps that provide accurate geometric supervision in both observed and unobserved regions.
Beyond establishing improved geometry as the inpainting basis, we further incorporate geometry guidance into the generative refinement loop to alleviate the shape-appearance ambiguities. We begin by constructing 3D visibility grid with scale-accurate depth, which provides more reliable visibility masks for inpainting compared to the noisy alpha maps. We also search for novel views by leveraging global 3D planes as object proxies, guiding the placement of novel viewpoints to maximize coverage of complete planar structures and thereby providing richer contextual cues for inpainting. Finally, we inpaint the novel views using video diffusion model (Ma al. 2025). while leveraging global 3D planes to modulate the color supervision for reducing cress-view conflicts.
Experiments on Replica (Straub et al.) 2019), ScanNet++ (Yeshwanth et al. 2023). DeepBlend- ing (Hedman et al. (018) and Mip-NeRI 360 (Barron et al. (022] demonstrate that our method consistently outperforms all baselines in both geometric and appearance reconstruction, with partic- ularly strong improvements in unobserved regions. Ablation studies further confirm the effective- ness of each proposed component. Morcover, our approach supports reconstruction from casually captured videos or text-generated single images, illustrated in Figs. [ and [ with significant po- tential in practical downstream applications in embodied AI (Huang et al. 2024b 2025c Jia et al.,
2024), robotics eLi.tal. 2024c ZZh et al. 2025), and more (Jiang al. 2024 Wang et al. (024b)
Our main contributions are summarized as follows:
We propose novel method that leverages the plane representation to derive scale-accurate geo- metric constraints, substantially improving 3D scene reconstruction even in unobserved regions.
We incorporate geometry guidance in the generative pipeline, which improves visibility mask es- timation, novel view selection, and multi-view consistency with video diffusion models, yielding reliable and consistent scene completion.
Extensive experiments demonstrate that our method achieves state-of-the-ee performance on mul- tiple datasets, with support for indoor, outdoor, single-view and unposed video reconstruction.
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2 RELATED WORK
2.1 SPARSE- VIEW 3DGS
3D Gaussian Splatting (KeG)) et al., 2023; Lu et al. 2024a Yu et al. 2024c) has demon- strated impressive reconstruction quality and training efficiency, but its performance deteriorates significantly when trained with only a few input views. To address this limitation, numerous ap- Kumaar & Vats 2025| Zhang et al. 2202b;; Wu et al.) 2025b; Hong et al. 2025 Huang et al.
2025b, Xu et al.) 2024, Paliwal et al., 2024: Zhao et al.] 2024 2025a Bao al. 2024| Liu
et al.
2024f; Yin et al. 2024; Xiao et al. 2024 et et al. 2024b Zheng et al. 2025) have been pro-
posed. For example, DNGaussian (Li al. (0244) and FSGS (Zhu et al. (2244) incorporate depth
regularization to suppress floaters in visible regions; yet, the scale ambiguity inherent in monocular depth estimators (Yang al. 2024) prevents them from providing reliable geometric supervision. MAtCha (Guédon et al. 20255 attempts to overcome this limitation by introducing scale-accurate
depth from structure- srructure-from--- rrom-motion (SIM) methods (Wang et al. 2024a Duisterhof al. 2025). Nev- ertheless, it still struggles to reconstruct con-overlap regions between input views. To address this issue, we propose to leverage the inherent extensibility of plane representations that propagate accurate depth estimates from overlapping regions to non-overlapping or even unobserved regions.
2.2
GENERATIVE PRIOR FOR 3DGS
Recent studies (Poole et al. 2022:
Xiong et al. 2023 Weber et al. 2024 Wu et al. 2024 Liu et al.
2024c
Mclas- -yriazi et al. 22024; Shih et al. 2024 Paul et al.] Cai et al. 2024 Chen et al,
2024b Yu et al. [2224a] Ni et al. 2025) have demonstrated the effectiveness of leveraging diffusion
models (Rombach et al., 2022; Liu et al. 2023) to provide powerful priors for 3D reconstruction.
Some methods (Raj al. 2025 Huang al. 2025a Chang et al. 2025) attempt to introduce 3D
geometry priors into the reconstruction process. More recent approaches (Liu et al. 2024a b Zhao
al.
2025b Gao al.] 2024 Zhou et al. 2025 Bao et al. 2025) Wu al.) 2025a c Fischer al.)
2025
Yin et al.,
2025; Zhong et al.) 2025) further employ video diffusion models (Blattmann et al,,
2023
Yu al. 2024b) enhance cross- view consistency. However, since these models primar-
ily rely on initial reconstructions to apply their generative power, they still suffer from numerous floating Gaussian artifacts and low-quality 3D geometry in their results, particularly in the inpainted regions, due to insufficient geometric constraints under sparse observations. To address this issue, we first apply scale-accurate depth supervision for both observed and non-observed regions to pro- vide robust geometric foundation, and integrate geometric guidance throughout the video diffusion models, leading to significantly improved reconstruction of both geometry and appearance.
2.3 PLANE ASSUMPTION IN RECONSTRUCTION
The Manhattan-world assumption (Coughlan & Yuille 1999). and in particular the plane assump- tion, has been widely adopted in the reconstruction of man-made environments. Several meth-
ods (Liu et al. 2024c| Guo et al., 2024: Mazur et al. 2024 Liu et al. 2025b: Pataki et al., 2025)
leverage this assumption in SfM and SLAM to improve matching accuracy, while other plane
construction approaches (Liu et al. 2019 Agarwala et al. 2022 Xie et al. 2022 Tan et al. 2023
Watson et al., 2024; Ye al. 2025 Liu al. 2025a) directly fit a set of planes model indoor
scenes. More recent studies Guo et al. 2022 Li et al. 2024b] Chen et al. 2024a Shi et al., 2025 incorporate the plane assumption to optimize 3D neural implicit representations MMildenhall
et al. 2020 Kerbl et al.] 2023). For instance, GeoGaussian (Li et al. 2024d) and IndoorGS (Ruan ct al. 2025) impose local planar constraints to regulate Gaussian splitting and movement, whereas PlanarSplatting (Tan et al. 2025) reconstructs 3D scenes as planar primitives directly from multi- view images. Different from these methods, our approach leverages the plane assumption to extract scale-accurate depth, which not only provides geometric guidance for optimizing the Gaussian rep- resentation but also facilitates the integration of generative prior, ultimately enabling precise scene reconstruction for both planar and non-planar structures across observed and unobserved regions.
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Training Views
Global 3D Planes Plane-aware Novel View Selection
Input Views
Global Plane
Global 3D Plane
Novel Views
Plane-aware Depth
Lrgh
Litrrcet
Novel View Inpainting
Render
Render
Video Diffusion Model
Raw RGB
Gaussian Field
Visibility Mask
Visibility Grid
first extract global 3D> planes from all training views and compute plane-aware depth maps (Section (.2]. Subsequently. construct visibility grid from these depth maps, select plane-aware novel views, inpaint their invisible regions, and incorporate the completed views back into the training set (Section 3.3
Figure 2: Overview of G4SPLAT. For each training loop (Section 3.4
3 METHOD
We propose G4SPLAT, method that integrates accurate geometry guidance with generative priors enhance 3D scene reconstruction. We begin by introducing the base model MAtCha (Guédon et al. 2025) and the overall training objective in Section Next, we present our plane-aware ge- ometry modeling in Section 3.2 followed by the geometry-guidde generative pipeline Section 3.3 Finally, we describe the overall training strategy in Section 3.4
3.1 BACKGROUND
2D Gaussian Splatting (2DGS) (Huang et al. 2024a) extends the original 3D Gaussian Splatting (3DGS) framework (Kerbl et al. 2023 collapsing 3D volumetric Gaussians into 2D anisotropic disks. Building on 2DGS, MAICha (Guédon et al.) (2025) introduces a chart alignment procedure that optimizes the chart parameters for each input view based the outputs of MASt3R-SM (Duisterhof) et al. 2025). The chart parameters from all views are then used to generate scale-aceurate depth maps, which serve absolute depth supervision for training 2DGS.
Given N input images { with its associated camera poses, the overall training objective of MAtCha combines an RGB reconstruction loss Ligh, the original 2DGS regularization loss Creg. and structure loss Lareet formulated as:
Lusal Ligh + freg + Estruct-
more detailed description each term is provided the Appendix B.3
However, the effectiveness of chart alignment heavily relies accurate image correspondences; in regions with poor or missing matches, noticeable errors occur, as shown in Fig. 3a
3.2 PLANE-AWARD GEOMETRY MODELING
Inspired by prior work (Mazur et al. 2024 Ye et al. 2025 we assume that planar regions within an image exhibit consistent normal directions, smooth geometry, and similar semantics. Based on this assumption, we extract plane masks from images by combining instance masks from SAM (Kirillov et al.) (203)) with normal maps generated from either depth-map gradients or a monocular predictor Ye et al. 2024). Specifically, we apply K K-means clustering to the normal map to obtain regions with coherent surface orientations. These regions subsequently fil- tered using SAM masks, and only those assigned the same instance label and exceeding predefined size threshold are treated valid 2D plane masks. Example results shown in Fig. 3a
Per-view 2D Plane Extraction
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Rix Rob
(b) Comparison alpha visibility (IM).
Fart Llcakiton
(a) Plane Visualization Cloud Comparison.
(c) Comparison
view (NXV) and plane-aware novel view (PNV).
Figure 3: Visualization of intermediate results. Our method addresses key issues in prior ap- proaches: (a) MArCha produces noticeable errors in non-overlapping regions (highlighted by cir- cles): (b) masks derived from alpha maps contain enrors in visible areas of novel views; and (c) naive novel view selection offers only local coverage, causing visible seams in the final reconstruction.
Global 3D Plane Estimation The 2D plane masks extracted from individual views are often over- segmented and lack global consistency, resulting in the same 3D plane being fragmented across multiple views. To address this issue, we leverage the 3D scene point cloud establish correspon- dences among local masks and merge them into globally consistent 3D planes, as shown in Fig. 3a
Specifically, for each per-view 2D plane mask, we collect the associated 3D points from the scene point cloud via projection. Two local planes are merged into the same global 3D plane if their associ- ated 3D point sets exhibit sufficient spatial overlap and have similar normal directions. By repeating this process across all views, we obtain set of global point collections {Px). each representing global 3D plane. Further details are in Appendix C.T. Each global 3D plane is represented as:
D n2 x - dç 0,
where n& E R3 is the unit normal vector and d2 E R is the offset. To robustly estimate the plane parameters, we select subset of high-confidence points Pk C. Pk. defined as those observed in at least different views. A 3D plane then fitted Pronf by RANSAC (Fischler & Bolles 1981):
min
CnIp+ de)
s.t. m
DE PEP
This procedure produces geometrically accurate and cross-view consistent 3D plane estimates, pro- viding reliable geometric basis for subsequent optimization.
Plane-Award Depth Map Extraction With the estimated global 3D planes, extract plane- aware depth map D for each view v. Let {P denote the set of M 2D plane masks in view v, each associated with global 3D plane Φxy- For each pixel E P." cast ray from the camera center o" along the ray direction r" (u), and compute its depth by intersecting the ray with the global 3D plane ox.:
o" de (u)
(u)
(4)
For the non-planar regions of _d that are visible in the input view, we retain the depth values es- timated by MAICha (Guédon et al., 2025 for those that are not visible, we adopt pre-trained monocular depth estimator (Yang al. 2024h predict a relative depth map D This relative map aligned to an absolute scale using the already computed plane region depths (DD) via a linear transformation:
D* a. D7 + bvs
where the scale ap and offset b, are estimated by leaat-squares fitting over pixels belonging to planar regions. The resulting depth map D" integrates geometry-consister consistent plane depths with re- fined monocular predictions in unobserved non-planar areas, yielding complete and scale-accurate plane-aware depth representation for each view. As illustrated in Fig. Bal this plane-aware depth significantly alleviates errors in non-overlapping regions when compared MAtCha.
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3.3 GEOMETRY-GUIDED GENERATIVE PIPELINE
Geometry-Guide Visibility As shown in Fig. 3b existing methods rely on inpainting masks derived from alpha maps, which often introduce errors within visible regions and thus degrade the inpainting results. To mitigate this issue, we employ scale-accurate plane-aware depth to model scene visibility using a visibility grid. Specifically, we first determine the 3D boundaries of the scene based on the depth maps from all training views. The scene is then discretized into voxel grid G. For each voxel, we determine its visibility by projecting its center to each training view and checking whether it falls within the valid depth range. A voxel is marked as visible (ie., visibility value = 1) if it lies within the observable depth range in at least one view. This visibility assessment is performed in parallel for all voxels, ensuring efficient construction of the visibility grid.
With the visibility grid, we render the visibility map for novel view using the corresponding GS- rendered depth map. Specifically, pixel-wise visibility is evaluated by casting a ray from the camera center through each pixel and uniformly sampling Q points along the ray up to the rendered depth. The visibility value V/ of each sample point is determined via nearest neighbor interpolation over the visibility grid g. The final per-pixel visibility (u) is computed as
V (u) =
(6)
indicating that a pixel is considered visible only all Q sampled points along its viewing ray are marked as visible in the visibility grid g.
Plane-Aware Novel View Selection As shown in Fig. 3c naive novel view selection strategies, such as the elliptical trajectory around the scene center (Wu et al.) (025c), tend to provide only limited local coverage. Inpainting results from these novel views often introduce noticeable artifacts in the final reconstruction due to multiview inconsistencies. To address this issue, we propose a plane-aware view selection strategy that ensures complete coverage of objects for the selected views by leveraging global 3D planes as object proxies. Global 3D planes generally provide sufficient structural and textural cues for object awareness enable reliable inpainting. For each global 3D plane, we use its centroid as the look-at target and search for camera center among the visible grid centers in the visibility grid. The selection process is guided by three objectives: maximizing coverage of plane points, minimizing distance to the plane, and encouraging alignment between the viewing direction and the plane normal. More details are described in Appendix C.2
Seometry-Guidee Inpainting For each novel view we render the raw RGB map I" (Eq, A2 along with the visibility mask V (Eq, 6). We then employ a pre-trained video diffusion model (Ma al. 2025). which takes input images reference { and { v"} as input to jointly inpaint the occluded regions across all views, producing the completed images {1²}.
Despite this joint inference, the inpainted results {î"} from existing generative models still exhibit multi-view inconsistencies, which can introduce artifacts during training (Zhong et al.) (025). To address this issue, we supervise each region by primarily relying on color information from a single view. For planar regions, we choose the view that provides the most complete observation of the corresponding plane, ensuring consistent inpainting. For non-planar regions, we use the first view in which the region becomes visible, which helps reduce cross- view inconsistencies. A more detailed discussion is provided in Appendix
3.4 OVERALL TRAINING STRATEGY
Our training pipeline consists of two stages: an initialization stage and a geometry-guine sonned generative training loop. This design ensures that reconstruction begins with reliable geometry and progres- sively improves both coverage and multi-view consistency.
In the initialization stage, we first apply chart aligmment in MAICha to obtain an initial depth map for each input view. From these depth maps, we estimate global 3D planes and compute plane- aware depth maps. as described in Section 3.2] The Gaussian parameters are then initialized from the resulting point cloud and optimized using these alane-aware depth maps, producing a baseline model with accurate geometry the regions observed by the input views.

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Published as a conference paper at ICLR 2026
Replicca
ScanNeif+
DeepBlending
Mip-NeRF 360
MAICha
GenFusion
Difix3D+
GuidedVD
See3D
Ours
GT
Figure 4: Qualitative comparison. Our approach achieves better appearance and geometry recon- struction with fewer Gaussian floaters in both observed and unobserved regions.
The second stage refines and extends the reconstruction through the iterative process described in Section 3.3 As illustrated in Fig. 2 each loop begins by constructing visibility grid from the current training views, followed by selecting novel viewpoints and inpainting their invisible regions; the inpainted novel views then merged into the training set. Subsequently, global 3D planes and plane-aware depths are recomputed, and the Gaussians are fine-tuned with updated supervision. Repeating this loop progressively recovers
regions and corrects geometric misalignments.
During each round of 2DGS training, our method adopts the same total loss formulation as MAtCha (Eq. (1), but enhances chart depth maps with plane-aware depth maps, thereby introducing stronger geometric constraints that lead to more accurate and consistent reconstructions. In our experiments, employ three generative training loops; further details are provided in Appendix C.7
EXPERIMENTS
We evaluate G4SPLAT on both geometric and appearance reconstruction sparse-view scenar- ios. Additionally, we present more experimental results in Appendix [A] failure cases and discuss the method's limitations in Appendix [ For more extensive qualitative results and video demon- strations, please refer to our project page.
4.1 SETTINGS
Datasets We evaluate our method on both synthetic and real-world datasets. The synthetic dataset is Replica (Straub al. 2019), which contains 8 indoor scenes. The real-world datasets include scenes from ScanNet++ (Yeshwanth et al., 2023), 3 scenes from DeepBlending (Hedman et al. 2018) and 9 scenes from Mip-NeRF 360 (Barron al. 2022).

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Table I: Quantitative comparison from 5 input views. Our method significantly outperforms all baselines across both reconstruction and rendering metrics. Top-3 results are highlighted as the first second and third.
Reconstruction
Rendering
Dataset
Method
CDL
F-Score
NC1
PSNR1
SSIM
LPIPS.
3DGS
16.61
27.72
64.34
18.29
0.744
0.254
2DGS
14.64
48.01
74.14
18.43
0.735
0.306
FSGS
18.17
26.87
64.16
19.19
0.766
0.259
InstantSplat
21.00
19.67
62.01
19.39
0.762
0.255
Replica
MAtCha
10.12
60.90
79.33
17.81
0.752
0.228
See3D
12.74
45.27
73.98
19.22
0.735
0.328
GenFusion
13.05
41.60
69.33
20.14
0.801
0.258
Difix3D+
13.71
43.11
65.34
19.42
0.779
0.231
GuidedVD
27.87
17.29
61.64
22.51
0.822
0.260
Ours
6.61
65.14
83.98
23.90
0.836
0.199
3DGS
16.60
31.92
65.35
14.28
0.696
0.372
2DGS
14.34
51.97
70.01
13.91
0.661
0.429
FSGS
23.80
27.86
64.53
14,80
0.731
0,362
InstantSplat
21.32
25.44
60.67
15,02
0.742
0.355
ScanNet++
MAtCha
11.55
62.98
73.61
13.58
0.677
0.351
See3D
13.03
53.65
70.39
14.76
0.684
0.426
GenFusion
10.68
47.15
66.27
16.12
0.726
0.347
Difix3D+
13.15
53.91
67.30
14.09
0.701
0.340
GuidedVD
25.35
16.67
60.48
17.90
0.807
0.336
Ours
6.34
67.12
77.45
18.69
0.792
0.314
3DGS
31.44
20.02
55.39
15.33
0.571
0.489
2DGS
25.60
23.81
63.82
14.89
0.556
0.506
FSGS
31.45
19.66
57.38
15.72
0.602
0.476
InstantSplat
33.78
17.99
57.91
15.00
0.569
0.483
DeepBlending
MAICha
22.36
26.80
67.92
14.74
0.558
0.465
Sce3D
31.34
22.68
63.18
15.00
0.552
0.537
GenFusion
30.70
22.37
58.70
16.20
0.626
0.468
Difix3D+
32.70
21.94
58.08
15.18
0.583
0.450
GuidedVD
43.28
15.95
59.21
16.32
0.618
0.481
Ours
20.72
28.02
72.04
16.76
0.645
0.440
For ScanNet++, DeepBlending, and Replica, we uniformly sample 100 images per scene. For the former two, we randomly select 5 images as input views and use the remaining 95 for testing. For Replica, we conduct experiments with 5, 10, and input views to assess performance under varying view sparsity (Appendix AA.3). while maintaining a consistent set of 85 test views across all settings. For Mip-NeRF 360, we follow the the 9-input-view configuration as established in prior work eWuet al.) 2024
Gao et al. 2024).
Metrics Reconstruction quality is quantified using Chamfer Distance (CD), F-Score, and Normal Consistency (NC), while rendering performance is measured via PSNR, SSIM, and LPIPS. As the Mip-NeRF 360 dataset lacks ground-truth meshes, we evaluate only the rendering performance for those scenes; for the remaining three datasets, we report the full suite of metrics. Further details are provided in Appendices and C.6
Baselines We compare our method with several representative basclines. These include the clas- sical Gaussian Splatting approaches 3DGS (Kerbl al. 2023) and 2DGS (Huang et al. 2024a). as well as state-of-the-aa sparse-view 3DGS methods FSGS (Zhu et al.) (2024), InstantSplat (Fan jet al. (024)) and MAICha (Guédon et al. 2025). We further evaluate against highly competitive recent approaches that integrate generative diffusion models into sparse-view 3DGS reconstruction. namely GenFusion (Wu et al., 2025c). Difix3D+ (Wu et al., 2025a) and GuidedVD (Zhong et al. 2025). In addition, implement variant of 2DGS augmented with the See3D (Ma et al. 2025). Notably, all baselines are augmented with MASt3R-SH (Duisterhof et al. 2025) to provide robust geometric initialization and improve performance in sparse-vies -iew scenarios.
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input view
1 input view
5 input views
5 input views
10 input views
10 input views
Figure 5: Any-view scene reconstruction. Our method demonstrates strong generalization across
diverse scenarios, including indoor and outdoor scenes, unposed scenes and even single-view scenes.
4.2 RESULTS
Novel View Synthesis Quality By incorporating ge- ometry guidance into the generative prior, our method achieves more accurate renderings in unobserved regions while producing fewer artifacts in observed regions, as illustrated in Figs. 4 A6 and A8 and quantified in Ta- bles and In contrast, other methods that leverage generative prior exhibit notable limitations. For exam- ple. Difix3D+ attains relatively good quality in observed regions but fails to handle unobserved areas. GenFusion, See3D, and GuidedVD can hallucinate content in unob- served regions; however, their completions are blurry and compromised by severe floaters, and they even degrade the reconstruction quality of observed gions. These comparisons highlight the effectiveness of our geometry-guided generative pipeline in maintaining high fidelity across both observed and unobserved regions.
Table 2:
Quantitative comparison from 9 input views on Mip-NeRF 360.
Method
PSNR 1
SSIM
LPIPS
MAtCha
16.44
0.453
0.389
GenFusion
17.82
0.452
0.439
Difix3D+
16.28
0.389
0.426
GuidedVD
16.78
0.438
0.499
See3D
16.92
0.443
0.451
Ours
18.66
0.515
0.371
Geometry Reconstruction Quality As shown in Figs. A6 and A8 Difix3D+, GenFusion, See3D and GuidedVID all suffer from severe appeaaapee ambiguifies: even when the ren- dered views appear visually plausible, the reconstructed geometry is of poor quality. In comparison, our approach yields more accurate geometry in unobserved regions and produces smoother, floater- free reconstructions in observed regions. This improvement stems from our plane-aware geometry modeling, which provides reliable depth supervision for both observed and unobserved areas, ensur- ing consistency across the entire scene. Quantitatively, Table [ shows that our method significantly outperforms all baselines on all reconstruction metrics across multiple datasets.
Any-Vies Scene Reconstruction As demonstrated in Figs. I Al and A2 our method ex- hibits robust performance across a wide range of scenarios, including indoor and outdoor scenes, single-view cases and unposed scenes such as YouTube videos. As shown in Table our method consistently outperforms all baselines regardless of the number of input views. Moreover, in scenes with complex lighting, where existing baselines struggle to achieve accurate reconstruction even with dense input views, our approach leverages accurate geometry guidance to effectively suppress errors caused by significant brightness variations across viewpoints, thereby producing high-quality reconstructions, as shown Fig. A2 In addition to achieving superior results on indoor scenes, shown in the quantitative and qualitative results the Mip-NeRF 360 dataset (Tables 2 and A3 AA7). our method also outperforms the baselines on outdoor, non-Manhattan, and less structured scenes. Furthermore, the Museum in Fig. and the Cat in Fig. 5 further demonstrate its capability to handle arbitrary 3D structures.
and Figs. and [A))
4.3 ABLATION STUDIES
We conduct ablation experiments on Replica dataset evaluate the contributions of the generative prior (GP), plane-aware geometry modeling (PM), and geometry-guiee guided generative pipeline (PP). As shown in Table and Fig. A4 make the following key observations:

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Published as a conference paper at ICLR 2026
Table 3: Ablation study.
Table 4: Running time comparison. (min))
Reconstruction
Rendering
Method
CD
PSNR1
GP
PM PP
CDL
F-Scoret
NC1
PSNRT
SSIMI
LPIPS
MA1Cha
11.57
32.4
See3D
15.42
14.50
58.6
x
x
10.60
59.17
79.95
17.85
0.751
0.228
GenFusion
15.66
16.49
41.4
x
9.46
56,99
77.58
19.63
0.740
0.295
Dif
17.79
68.7
x
8.73
64.96
80.55
17.63
0.752
0,219
GuidedVD
24.29
19.02
141.4
7.56
62.36
80.89
21.88
0.810
0.221
Ours
8.77
20.26
73.3
6.61
65.14
83.98
23.90
0.836
0.199
Ours (DS)
9.33
43.5
Incorporating generative prior (GP) alone improves rendering quality but yields limited gains in geometry reconstruction. LPIPS, F-Score, and NC even decline, as GP alone tends to produce averaged, blurry results for the unseen areas. This indicates that directly introducing generative prior fails to perform expected and leads to shape-appearance ambiguities. Moreover, as shown in Fig. A4 our method is compatible with different generative diffusion models, achieving strong performance across them.
2. Adding plane-aware geometry modeling (PM), either alone or in combination with generative prior (GP), significantly improves geometry reconstruction. Moreover, incorporating PM along with GP brings notable gains in rendering quality. This demonstrates that accurate geometry guidance effectively provides a clean geometry basis and suppresses Gaussian floaters, thereby enabling the generative model to fulfiil its intended role.
3. Incorporating geomelry-guided generative pipeline (PP) further enhances both rendering fidelity and geometric accuracy. This indicates that geometry guidance provides more accurate visibil- ity masks, novel views with broader plane coverage, and consistent color supervision, thereby improving the generative process by mitigating multi-view inconsistencies.
4.4 DISCUSSION ON PLANE REPRESENTATION
Our method leverages plane representation to obtain scale-accurate geometry guidance. We adopt this representation for three main reasons. First, planar structures are common in man-made envi- ronments and often occupy large portion of the image, such as floors, walls, and tabletops. Second, the plane representation exhibits strong generalization. By fitting global 3D planes to accurate point clouds obtained in overlapping regions of input views, we can extend reliable depth estimation to non-overlapping or even unobserved planar regions. The accurate planar depth also enables linear alignment of monocular depth maps, improving depth accuracy of unobserved non-planar regions as well. Finally. the plane representation is simple, computationally efficient, and memory- memory-friendly. As shown in Table 4 our method achieves high-quality reconstruction with a runtime comparable to other approaches that employ generative prior. Our accelerated variant, Ours (DS), which down- samples the initial Gaussians, substantially reduces runtime while still outperforming all baselines.
Furthermore, method maintains robust performance in non- non-planar or less structured scenes be- cause it is a strict enhancement of the base model. This stems from our design that applies tailored supervision on regions according their geometric characteristics: for planar regions, reconstruc- tion is improved by leveraging accurate plane depth supervision across both observed and unob- served areas; for non-planar regions, observed regions preserve the base model's original supervi- sion to ensure reliability, and unobserved portions benefit from linearly aligned monocular depth as an effective fallback where direct supervision is absent. Experiments on the Mip-NeRF 360 dataset validate these advantages. Additional discussions are provided in Appendices A.2 and
5 CONCLUSION
We present G4SPLAT, a geometry-guided generative framework for 3D scene reconstruction. The method first leverages plane representations to derive scale-accurate geometric constraints, and then integrates these constraints throughout the generative pipeline to effectively mitigate shape-appearance ambiguities. Extensive experiments demonstrate that our method consistently outperforms existing approaches in both geometry and appearance reconstruction, with notable im- provements in unobserved regions. Morcover, our method naturally supports unposed video and single-view inputs, offering strong potential for practical applications in real-world scenarios.
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REFERENCES
Samir Agarwala, Linyi Jin, Chris Rockwell, and David F Fouhey. Planeformers: From sparse view planes to 3d reconstruction. In European Conference on Computer Vision (ECCV), 2022.
Chong Bao, Xiyu Zhang, Zehao Yu, Jiale Shi, Guofeng Zhang, Songyou Peng, and Zhaopeng Cui. Free360: Layered gaussian splatting for unbounded 360-degree view synthesis from extremely sparse and unposed views. Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Zhenyu Bao, Guibiao Liao, Kaichen Zhou, Kanglin Liu, Qing Li, and Guoping Qiu. Loopsparsegs: Loop based sparse-view friendly gaussian splatting. arXiv preprint arXiv22408.00254 2024.
Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul Srinivasan, and Peter Hedman. Mip- nerf 360: Unbounded anti-aliased neural radiance fields. In Conference Computer Vision and Pattern Recognition (CVPR), 2022.
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets, arXiv preprint arXiv:231..15227 2023.
Xudong Cai, Yongcai Wang, Zhaoxin Fan, Deng Haoran, Shuo Wang, Wanting Li, Deying Li, Lun Luo, Minhang Wang, and Jintao Xu. Dust tower: Coarse-to-fine photo-realistic scene recon- struction from sparse uncalibrated images. arXiv preprint arXiv:24/2. 2024.
Jiahao Chang, Chongjie Ye, Yushuang Wu, Yuantao Chen, Yidan Zhang, Zhongjin Luo, Chenghong Li, Yihao Zhi, and Xiaoguang Han. Reconviagen: Towards accurate multi-view 3d object recon- struction via generation. arXiv preprint arXiv:2510.23306 2025.
Danpeng Chen, Hai Li. Weicai Ye, Yifan Wang, Weijian Xie, Shangjin Zhai Nan Wang. Haomin Liu, Hujun Bao, and Guofeng Zhang. Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction. IEEE Transactions Visualization and Computer Graphics, 2024a.
Shen Chen, Jiale Zhou, and Lei Li. Optimizing 3d gaussian splatting for sparse viewpoint scene reconstruction. arXiv preprint arXiv:2409.03213. 2024b.
James M Coughlan and Alan Yuille. Manhattan world: Compass direction from a single image by bayesian inference. In International Conference on Computer Vision (ICCV), 1999.
Bardienus Pieter Duisterhof, Lojze Zust, Philippe Weinzaepfel, Vincent Leroy, Yohann Cabon, and Jerome Revaud. MASt3r-ffm: a fully-integrated solution for unconstrained structure-from: motion. In International Conference on 3D Vision (3DV), 2025.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In International Conference Machine Learning (ICML), 2024.
Zhiwen Fan, Kairun Wen, Wenyan Cong, Kevin Wang, Jian Zhang, Xinghao Ding, Danfei Xu, Boris Ivanovic, Marco Pavone, Georgios Pavlakos, Zhangyang Wang, and Yue Wang. Instantsplat: Sparse-view gaussian splatting in seconds. arXiv preprint arXiv:2403.20309 2024.
Tobias Fischer, Samuel Rota Bulò, Yung-Hsu Yang, Nikhil Keetha, Lorenzo Porzi. Norman Müller, Katja Schwarz, Jonathon Luiten, Mare Pollefeys, and Peter Kontschieder. FlowR: Flowing from sparse to dense 3d reconstructions. In International Conference on Computer Vision (ICCV), 2025.
Martin A. Fischler and Robert C. Bolles. Random sample consensus: paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 1981.
Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo -rrttin,rrrala,, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole. Cat3d: Create anything in 3d with miewi-view diffusion models. In Advances in Newral Information Processing Systems (NeurlPS), 2024.

---

Published as a conference paper at ICLR 2026
Antoine Guédon, Tomoki Ichikawa, Kohei Yamashita, and Ko Nishino. Matcha gaussians: Atlas of charts for hugh-quality geometry and photorealism from sparse views. Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, and Xiaowei Zhou. Neural 3d scene reconstruction with the manhattan-world assumption. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Haoyu Guo, Sida Peng, Ting Shen, and Xiaowei Zhou. Planestereo: Plane-aware multi-view stereo. Machine Intelligence Research, 2024.
Peter Hedman, Julien Philip. True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Bros- low, Deep blending for fice-viewpoint image-based rendering. ACM Transactions on Graphics (ToG), 2018.
Yuhang Hong, Bo Tao, and Zeyu Gong. Sharegs: Hole completion with sparse inputs based on reusing selected scene information. Pattern Recognition, 2025.
Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In ACM SIGGRAPH Eurographics Symposium on Computer Animation (SCA), 2024a.
Binbin Huang. Haobin Duan, Yiqun Zhao, Zibo Zhao, Yi Ma, and Shenghua Gao. Cupid: Pose- grounded generative 3d reconstruction from a single image. arXiv preprint arXiv:2510.20776, 2025a.
Han Huang, Yulun Wu, Chao Deng, Ge Gao, Ming Gu, and Yu-Shen Liu. Fatesgs: Fast and accurate sparse-view surface reconstruction using gaussian splatting with depth-feature consistency. In AAAI Conference on Artificial Intelligence (AAAI), 2025b.
Jiangyong Huang, Silong Yong. Xiaojian Ma. Xiongkun Linghu, Puhao Li, Yan Wang, Qing Li, Song-Chun Zhu, Baoxiong Jia, and Siyuan Huang. An embodied generalist agent 3d world. In International Conference Machine Learning (ICML), 2024b.
Jiangyong Huang, Baoxiong Jia, Yan Wang, Ziyu Zhu, Xiongkun Linghu, Qing Li, Song-Chun Chun Zhu, and Siyuan Huang. Unveiling the mist over 3d vision-language understanding: Object-centric evaluation with chain-of-analysis. In Conference on Computer Vision and Pattern Recognition (CVPR), 2025c.
Baoxiong Jia, Yixin Chen, Huangyue Yu, Yan Wang, Xuesong Niu, Tengyu Liu, Qing Li, and Siyuan Huang. Sceneverse: Scaling vision-language learning for grounded scene understanding. In European Conference Computer Vision (ECCV), 2024.
Nan Jiang, Zimo He, Zi Wang, Hongjie Li, Yixin Chen, Siyuan Huang, and Yixin Zhu. Autonomous character-scene interaction synthesis from text instruction. In SIGGRAPH Asia 2024 Conference Papers, 2024.
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splat- ting for real-time radiance field rendering. In ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA), 2023.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. Segment anything. In International Conference on Computer Vision (ICCV), 2023.
Jonas Kulhanek, Songyou Peng, Zuzana Kukelova, Mare Pollefeys, and Torsten Sattler. WildGaus- sians: 3D gaussian splatting in the wild. In Advances in Neural Information Processing Systems (NeurIPS), 2024.
Raja Kumar and Vanshika Vats. Few-shot novel view synthesis using depth aware gaussian splatting. In European Conference Computer Vision Workshop, 2025.
12

---

Published as a conference paper at ICLR 2026
Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Xin Ning, Jun Zhou, and Lin Gu. Dngaussian: Optimiz- ing sparse-view 3d gaussian radiance fields with global- depth normalization. In Conference Computer Vision and Pattern Recognition (CVPR), 2024a.
Jing Li, Jinpeng Yu, Ruoyu Wang, and Shenghua Gao. Pseudo- Pseudo-plane regularized signed distance field for neural indoor scene reconstruction. International Journal of Computer Vision (IJCV), 2024b.
Puhao Li, Tengyu Liu, Yuyang Li, Muzhi Han, Haoran Geng, Shu Wang, Yixin Zhu, Song Chunn Zhu, and Siyuan Huang. Ag2manip: Learning novel manipulation skills with agent-agnostic visual and action representations. arXiv preprint arXiv:2404. 17521, 2024c.
Yanyan Li, Chenyu Lyu, Yan Di, Guangyao Zhai, Gim Hee Lee, and Federico Tombari. Geogaus- sian: Geometry-awenn gaussian splatting for scene rendering. In European Conference Com- puter Vision (ECCV), 2024d.
Yuyang Li, Bo Liu, Yiran Geng, Puhao Li, Yaodong Yang, Yixin Zhu, Tengyu Liu, and Siyuan Huang. Grasp multiple objects with one hand. In International Conference on Intelligent Robots and Systems (IROS), 2024e.
Chen Liu, Kihwan Kim, Jinwei Gu, Yasutaka Furukawa, and Jan Kautz. Planerenn: 3d plane de- tection and reconstruction from single image. In Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Fangfu Liu, Wenqiang Sun, Hanyang Wang. Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, and Yueqi Duan. Reconx: Reconstruct any scene from sparse views with video diffusion model. arXiv preprint vrXiv2240..16767 2024a.
Jiachen Liu, Rui Yu, Sili Chen, Sharon X Huang, and Hengkai Guo. Towards in-the-wild 3d plane reconstruction from single image. In Conference Computer Vision and Pattern Recognition (CVPR), 2025a.
Kunhao Liu, Ling Shao, and Shijian Lu. Novel view extrapolation with video diffusion priors. arXiv 14208, preprint arXiv:2411. 142008 2024b.
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick.
Zero-I-to-3 Zero-shot one image to 3d object. In International Conference on Computer Vision (ICCV), 2023.
Shuhong Liu, Heng Zhou, Liuzhuozheng Li, Yun Liu, Tianchen Deng, Yiming Zhou, and Mingrui Li. Structure gaussian slam with manhattan world hypothesis. arXiv preprint arXiv:2405.20031, 2024c.
Shuhong Liu, Tianchen Deng, Heng Zhou, Liuzhuozheng Li, Hongyu Wang, Danwei Wang, and Mingrui Li. Mg-slam: Structure gaussian splatting slam with manhattan world hypothesis. JEEE Transactions Automation Science and Engineering, 2025b.
Xi Liu, Chaoyi Zhou, and Siyu Huang. 3dgs-enhancer: Enhancing unbounded 3d gaussian splatting with view-consistent 2d diffusion priors. In Advances in Neural Information Processing Systems (NeurIPS), 2024d.
Xinhang Liu, Jiaben Chen, Shiu-hong Kao, Yu-Wing Tai, and Chi-Keung Tang. Deceptive- nerf/3dgs: Diffusion ceauusionnngeertted cecudo-observttions for high-quality sparse-view reconstruction. In European Conference Computer Vision (ECCV), 2024e.
Zhaoliang Liu, Jinhe Su, Guorong Cai, Yidong Chen, Binghui Zeng, and Zongyue Wang. Georgs: Geometric regularization for real-time novel view synthesis from sparse inputs. IEEE Transac- fions on Circuits and Systems for Video Technology, 2024f.
Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang. Dahua Lin, and Bo Dai. Scaffold- gs: Structured 3d gaussians for view-adaptive rendering. In Conference on Computer Vision and Patlern Recognition (CVPR), 2024a.
13

---

Published as a conference paper at ICLR 2026
Ziqi Lu, Heng Yang, Danfei Xu, Boyi Li, Boris Ivanovic, Marco Pavone, and Yue Wang. Lora3d: Low-rank self-calibration of 3d geometric foundation models. In International Conference on Learning Representations (ICLR), 2024b.
Baorui Ma, Huachen Gao, Haoge Deng, Zhengxiong Luo, Tiejun Huang, Lulu Tang, and Xinlong Wang. You see it, you got it: Learning 3d creation pose-free videos at scale. In Conference Computer Vision and Pattern Recognition (CVPR), 2025.
Kirill Mazur. Gwangbin Bae, and Andrew Davison. Superprimitive: Scene reconstruction primitive level. In Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
Luke Melas- SoriasK,riazzi,, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, and Filippos Kokkinos. Im-3d: Iterative multiview diffusion and reconstruction for high-quality 3d generation. In International Conference Machine Learning (ICML), 2024.
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), 2020.
Junfeng Ni, Yu Liu, Ruijie Lu, Zirui Zhou, Song- Zhu, Yixin Chen, and Siyuan Huang. Decom- positional neural scene reconstruction with generative diffusion prior. In Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Avinash Paliwal, Wei Ye, Jinhui Xiong. Dmytro Kotovenko, Rakesh Ranjan, Vikas Chandra, and Nima Khademi Kalantari. Coherentgs: Sparse novel view synthesis with coherent 3d gaussians. In European Conference on Computer Vision (ECCV), 2024.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), 2019.
Zador Pataki, ular surface priors for robust ceuucture-frommmotiiee In Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Sarlin, Johannes L Schönberger, and Marc Pollefeys. Mp-sfm: Monoc-
Soumava Paul, Prakhar Kaushik, and Alan Yuille. Gaussian scenes: Pose-free sparse-view scene reconstruction using depth-enhanced diffusion priors. arXiv preprint arXiv:441.11566,, 2024.
Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. In International Conference Learning Representations (ICLR), 2022.
Kevin Raj, Christopher Wewer, Raza Yunus, Eddy Ilg, and Jan Eric Lenssen. Spurfies: Sparse-view surface reconstruction using local geometry priors. In International Conference 3D Vision 2025, 2025.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Cong Ruan, Yuesong Wang, Tao Guan, Bin Zhang, and Lili Ju. Indoorgs: Geometric cues guided gaussian splatting for indoor scene reconstruction. In Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Hermaan, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, et al. Zeronvs: Zero-shot 360-degree view synthesis from single image. In Conference on Computer Vision and Pattern Recognition (0VPR),2024.
Yuang Shi, Simone Gasparini, Géraldine Morin, Chenggang Yang, and Wei Tsang Ooi. Sketch and patch: Efficient 3d gaussian representation for man man-made scenes. In Proceedings of the 17th International Workshop IMmersive Mixed and Virtual Environment Systems, 2025.
14

---

Published as a conference paper at ICLR 2026
Meng-Li Shih, Wei-Chiu Ma, Lorenzo Boyice, Aleksander Holynski, Forrester Cole, Brian L. Cur- less, and Janne Kontkanen. Extranerf: Visibility-awwe aware view extrapolation of neural radiance fields with diffusion models. In Conference Computer Vision and Pattern Recognition (CVPR), 2024.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556 2014.
Julian Straub, Thomas Whelan, Lingni Ma, Yufan Chen, Erik Wijmans, Simon Green, Jakob J. Engel, Raul Mur-Artal, Carl Ren, Shobhit Verma, Anton Clarkson, Mingfei Yan, Brian Budge, Yajie Yan, Xiaqing Pan, June Yon, Yuyang Zou, Kimberly Leon, Nigel Carter, Jesus Briales, Tyler Gillingham, Elias Mueggler, Luis Pesqueira, Manolis Savva, Dhruv Batra, Hauke M. Strasdat, Renzo De Nardi, Michael Goesele, Steven Lovegrove, and Richard Newcombe. The Replica dataset: A digital replica of indoor spaces. arXiv preprint arXiv: 1906.05977 2019.
Bin Tan, Nan Xue, Tianfu Wu, and Gui-Song Xia. Nope-sace sac: Neural one- one-plane ransac for sparse- view planar 3d reconstruction. Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2023.
Bin Tan, Rui Yu, Yujun Shen, and Nan Xue. Planarsplatting: Accurate planar surface reconstruction in 3 minutes. In Conference Computer Vision and Pattern Recognition (CVPR), 2025.
Matias Turkulainen, Xuqian Ren, laroslav Melekhov, Otto Seiskari, Esa Rahtu, and Juho Kannala. Dn-splatter: Depth and normal priors for gaussian splatting and meshing. In Proceedings of 2025.
Winter Conference on Applications of Computer Vision (WACV), 2025.
Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geo- metric 3d vision made easy. In Conference on Computer Vision and Pattern Recognition (CVPR), 2024a.
Zan Wang, Yixin Chen, Baoxiong Jia, Puhao Li, Jinlu Zhang, Jingze Zhang, Tengyu Liu, Yixin Zhu, Wei Liang, and Siyuan Huang. Move you say, interact you Language-guider human motion generation with scene affordance. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024b.
Jamie Watson, Filippo Alcotti, Mohamed Sayed. Zawar Qureshi. Oisin Mac Aodha, Gabriel Bros- tow, Michael Firman, and Sara Vicente. Airplanes: Accurate plane estimation via 3d-consistent embeddings. Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
Ethan Weber, Aleksander Holynski, Varun Jampani, Saurabh Saxena, Noah Snavely, Abhishek Kar, and Angjoo Kanazawa. Nerfiller: Completing scenes via generative 3d inpainting. In Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
Jay Zhangjie Wu, Yuxuan Zhang, Haithem Turki, Xuanchi Ren, Jun Gao, Mike Zheng Shou, Sanja Fidler, Zan Gojeic, and Huan Ling. Difix3d+: Improving 3d reconstructions with single-step diffuuson models. In Conference on Computer Vision and Pattern Recognition (CVPR), 2025a.
Jiang Wu, Rui Li, Yu Zhu, Rong Guo, Jinqiu Sun, and Yanning Zhang. Sparse2dgs: Geometry- prioritized gaussian splatting for surface reconstruction from sparse views. Conference on Computer Vision and Pattern Recognition (CVPR), 2025b.
Rundi Wu. Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, and Aleksander Holynski. Reconfusion: 3d reconstruction with diffusion priors In Conference on Computer Vision and Pattern Recogni- tion (CVPR), 2024.
Sibo Wu, Congrong Xu, Binbin Huang, Geiger Andreas, and Anpei Chen. Genfusion: Closing the loop between reconstruction and generation via videos. Conference on Computer Vision and Pattern Recognition (CVPR), 2025c.
Yuru Xiao, Deming Zhai, Wenbo Zhao, Kui Jiang, Junjun Jiang, and Xianming Liu. Mcgs: Mul- tiview consistency enhancement for sparse-view 3d gaussian radiance fields. arXiv preprint arXiv:2410.11394 2024.
15

---

Published as a conference paper at ICLR 2026
Yiming Xie, Matheus Gadelha, Fengting Yang, Xiaowei Zhou, and Huaizu Jiang. Planarrecon: Real-time 3d plane detection and reconstruction from posed monocular videos. Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Haolin Xiong, Sairisheek Muttukuru, Rishi Upadhyay, Pradyumna Chari, and Achuta Kadambi. Sparsegs: Real-time 360 sparse view synthesis using gaussian splatting. arXiv preprint arXiv:2312.00206, 2023.
Wangze Xu, Huachen Gao, Shihe Shen, Rui Peng, Jianbo Jiao, and Ronggang Wang. Mvpgs: Exca- vating multi-view priors for gaussian splatting from sparse input views. In European Conference Computer Vision (ECCV), 2024.
Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. In Advances in Neural Information Processing Systems (NeurIPS), 2024.
Zesong Yang. Bangbang Yang, Wengi Dong. Chenxuan Cao, Liyuan Cui, Yuewen Ma, Zhaopeng Cui, and Hujun Bao. Instascene: Towards complete 3d instance decomposition and reconstruction from cluttered scenes. In International Conference Computer Vision (ICCV), 2025.
Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Zuo. Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, and Xiaoguang Han. Stablenormal: Reducing diffusion variance for stable and sharp normal. ACM Transactions on Graphics (TOG), 2024.
Hanqiao Yc, Yuzhou Liu, Yangdong Liu, and Shuhan Shen. Neuralplane: Structured 3d reconstruc- tion in planar primitives with neural fields. In International Conference on Learning Representa- fions (ICLR), 2025.
Chandan Yeshwanth, Yuch-Cheng Liu, Matthias Nießner, and Angela Dai. Scannet++: A high- fidelity dataset of 3d indoor scenes. In International Conference Computer Vision (ICCV), 2023.
Ruihong Yin, Vladimir Yugay, Yue Li, Sezer Karaoglu, and Theo Gevers. Fewviewgs: Gaussian splatting with few view matching and multi-stage training. In Advances in Neural Information Processing Systems (NeurIPS), 2024.
Xingyilang Yin, Qi Zhang, Jiahao Chang, Ying Feng, Qingnan Fan, Xi Yang, Chi-Man Pun, Huaqi Zhang, and Xiaodong Cun. Gsfixer: Improving 3d gaussian splatting with reference-guides video diffusion priors. arXiv preprint arXiv:2508, 250.:2550000667, 2025.
Hanyang Yu, Xiaoxiao Long, and Ping Tan. Lm-gaussian: Boost sparse-view 3d gaussian splatting with large model priors. arXiv preprint Stti::20090034555 2024a.
Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien- Tsin Wong, Ying Shan, and Yonghong Tian. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis. arXiv preprint arXiv:2409.0200 2024b.
Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, and Andreas Geiger. Monosdf: Ex- ploring monocular geometric cues for neural implicit surface reconstruction. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias- free 3d gaussian splatting. In Conference on Computer Vision and Pattern Recognition (CVPR), 2024c.
Dongbin Zhang, Chuming Wang. Weitao Wang, Peihao Li, Minghan Qin, and Haoqian Wang. Gaus- sian in the wild: 3d gaussian splatting for unconstrained image collections. In European Confer- ence on Computer Vision (ECCV), 2024a.
Jiawei Zhang, Jiahe Li, Xiaohan Yu, Lei Huang, Lin Gu, Jin Zheng, and Xiao Bai. Cor-gs: Sparse- view gaussian splatting via co-regularization. European Conference on Computer Vision (ECCV). 2024b.
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Kai Zhang, Germot Riegler, Noah Snavely, and Vladlen Koltun. Nerf++: Analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.04492, 2020.
Chen Zhao, Xuan Wang, Tong Zhang, Saqib Javed, and Mathieu Salzmann. Self-ensembling gaus- sian splatting for few-shot novel view synthesis. In International Conference on Computer Vision (ICCV), 2025a.
Liang Zhao, Zehan Bao, Yi Xie, Hong Chen, Yaohui Chen, and Weifu Li. Tsgaussian: Se- mantic and depth-guided target-specific gaussian splatting from sparse views. arXiv preprint arXiv:2412. 10051, 2024.
Yuyang Zhao, Chung-Chine Lin, Kevin Lin, Zhiwen Yan, Linjie Li, Zhengyuan Yang, Jianfeng Wang, Gim Hee Lee, and Lijuan Wang. Genxd: Generating any 3d and 4d scenes. International Conference Learning Representations (ICLR), 2025b.
Yulong Zheng, Zicheng Jiang. Shengfeng He, Yandu Sun, Junyu Dong, Huaidong Zhang, and Yong Du. Nexusgs: Sparse view synthesis with epipolar depth priors in 3d gaussian splatting. In Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Peiyuan Zhi, Peiyang Li, Jianqin Yin, Baoxiong Jia, and Siyuan Huang. Learning unified force and position control for legged loco-manipulation. arXiv preprint arXiv:2505.2002. 2025.
Yingji Zhong, Zhihao Li, Dave Zhenyu Chen, Lanqing Hong, and Dan Xu. Taming video diffusion prior with scene- green-groun guidance for 3d gaussian splatting from sparse inputs. Conference Computer Vision and Pattern Recognition (CVPR), 2025.
Jensen (Jinghao) Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. arXiv preprint arXiv:2503. 14489, 2025.
Zehao Zhu, Zhiwen Fan, Yifan Jiang, and Zhangyang Wang. Fsgs: Real-time few-shot view synthe- sis using gaussian splatting. In European Conference Computer Vision (ECCV), 2024.
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APPENDIX
This appendix provides additional details and analyses that complement the main paper. Appendix A presents extended experimental results, including single-view and dense-view reconstruction exper- iments, performance under varying numbers of input views, and additional qualitative results. Ap- pendix [ offers further background information our approach. Appendix desccribs additional training and implementation details to facilitate reproducibility. Appendix ure cases and limitations of our method. Finally, Appendix [] provides statement on the usage of large language models (LLMs) during the development of this work.
D discusses typical fail-
A MORE EXPERIMENT RESULTS
A.1 SINGLE-VIE VIEW RECONSTRUCTION
For the single-view reconstruction experiment, model (Esser et al. 2024) as input. We observe that inpainting-barn video diffusion models, such as See3D (Ma et al. (025), are less effective at synthesizing large unseen regions in scene when only a single reference view is available. In contrast, camera-control video diffusion models like Stable Virtual Camera (Zhou al.) 2025) perform significantly better in this setting. more de- tailed discussion on the choice of generative models is provided in Appendix [C.4] Based on these observations, we adopt Stable Virtual Camera as the generative prior in our method: ployed to generate multiple novel views conditioned on the input single-view image, and these views are then used to train our model. Additional single-view reconstruction results are shown in Fig. Al
image generated by a text-to-imaae
first em-
input View
Geometry View
Appeerance View
Geometry View
Appeerance View
Geometry View
Appearance View
Figure A1: More results single-view reconstruction. Our method achieves high-quality geom- etry reconstruction (i.e., geometry view) and realistic texture recovery (i.e., appearance view). The appearance view is obtained by rendering the exported colored mesh in Blender.
A.2 DENSE- VIEW RECONSTRUCTION
Our method not only outperforms the baselines under the sparse-view setting, but also achieves superior results when dense input views are available. Methods based on Gaussian Splatting of- struggle reconstruct regions with strong specularities and reflections in scenes with complex lighting (Zhang al., 2024a Kulhanek et al. 2024). This difficulty arises because the color in such regions varies significantly across different viewpoints, making it challenging for explicit represen- tations like Gaussian to fit them. In contrast, by incorporating scale-accurate geometry supervision, our approach effectively mitigates large color variations across views and produces accurate geom- etry reconstructions. As shown in Fig. A2 for scene with 383 input views, our reconstruction results are substantially better than those of the baselines.
Although MAICha can obtain reasonable depth supervision in chart views through chart alignment, requires maintaining separate chart representation for each chart view during the alignment process, introduced in Appendix [B.2] This results in high memory consumption, allowing only about 20 chart representations to be mantained on an NVIDIA 3090 GPU. contrast, our method leverages global 3D planes, each stored compactly plane equation (ng, de), which requires
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Input Views
3DGS
2DGS
MAtCha
Ours
Figure A2: Dense view reconstruction comparison.
minimal memory overhead. Moreover, since all computations are based on projections, our approach efficiently obtain scale-accurate depths for all input views by exploiting CUDA parallelization. All methods in Fig. A2 are evaluated on single NVIDIA 3090 GPU.
A.3 PERFORMANCE UNDER DIFFERENT NUMBERS OF VIEWS
We further assess the robustness of our method under varying numbers of input views. As sum- marized in Table Al our approach consistently outperforms all baselines in both rendering quality and geometric accuracy, regardless of the number input views. particular, Fig. [A]] illustrates that method achieves markedly superior results when dense views available, while Fig. A1 demonstrates that it still delivers strong reconstructions even with a single input view. These results highlight the effectiveness and generalizability of our design across diverse input-view scenarios.
Table A1: Quantitative results on varying numbers of input views in the Replica dataset. Our method consistently outperforms baselines regardless of the number of input views.
Reconstruction (CDL NCT)
Rendering (PSNRTALPIPSL)
Method
5 views
10 views
15 views
5 views
10 views
15 views
2DGS
14.64 / 74.14
9.37 81.24
7.17 85.33
18.43 / 0.306
21.79 / 0.207
25.30/ 0.139
FSGS
18.17 / 64.16
13.97 / 68.76
12.64/71.22
/.219
22.50/0.179
25.84 / 0.127
MArCha
11.10/81.00 81.20
7.35 83.99
6.03 85.67
17.85 0.228
21.26/0.153
25.00 0.109
Sec3D
12.74/73.09
9.22 80.44
7.40 84.22
19.22/0.33
22.73 0.240
25.56 0.183
GenFusion
13.05 69.33
10.04 74.03
8.88/ 76.57
20.14/0.258
23.90/0.183
26.48 0.138
DD
13.71
10.15 68.43
7.97 77..73
19.42/0.231
22.68 0.165
26.04 0.122
GuidedVD
27.87 /11.64
20.30 64.53
16.62 68.54
22.51 0.266
25,63 0.205
27.91/0.11
Ours
6.61 833998
4.88 85.49
3.98 87.25
23.9070.199
27.48 0.140
30.22 0.094
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A.4 MORE QUALITATIVE RESULTS
Additional qualitative results are presented in Figs. A7 and A8 These results further demonstrate
that combining geometry guidance with the generative prior effectively alleviates shape-appearance ambiguities, leading to significant improvements in both geometry reconstruction and rendering quality, particularly regions that are unobserved by the input views.
B BACKGROUND
B. ALPHA RENDERING 2DGS
In 2DGS (Huang et al. 2024a), the value at point (u,v) within the disk of 2D Gaussian is defined by the Gaussian function:
u + 2
(u)) = exp
(A1)
2
Each 2D Gaussian is associated with opacity and view-dependent color с represented using spherical harmonics. During rasterization, Gaussians are depth-sorted and composited into the final image using front-to-back alpha blending:
N
i-1
c(x) = Σ caa (u(x(
[1 II
روg ر59) (u(x))]
(A2)
2=1
j=1
where u(x) denotes the (u, v) coordinate obtained by intersecting the camera ray through pixel x with the corresponding 2D Gaussian disk.
Similarly, the depth map can be computed in the same manner, by replacing colors with the z-buffer values of the corresponding Gaussians.
d(x)
Edass (u(x)
II
[1 j=1
0ggs (u(x))],
(A3)
i=1
where d; represents the b-buffer value of the i-th 2D Gaussian disk.
B.2 CHART REPRESENTATION AND ALIGNMENT IN MATCHA
MAtCha (Guédon et al. 2025) represents each chart with a lightweight deformation model designed to balance flexibility and efficiency. Each chart is parameterized by a sparse 2D grid of Icarnable features in UV space, together with small MLP that maps these features to 3D deformation vectors. To properly handle depth discontinuities across object boundaries, the model further augmented with depth-dependent features, allowing points different depths be deformed independently, even they are close in UV space. This design preserves high-frequency details from the initial charts while capturing low-frequency, object-dependent deformations.
Fach chart is initialized from a monocular depth map estimated by monocular depth predic- tor (Yang al. 2024). To ensure geometric consistency across views, MAtCha then performs an alignment stage that jointly optimizes chart deformations under multiple objectives.
Fitting loss To align charts with sparse SfM points, MAtCha minimizes their distances to the deformed charts:
R- m
Cu
Σ
((u)))))(()) - pu a log(C,)
(A4)
where (u) denotes the deformed 3D position of UV coordinate chart and pi is the 3D po- sition of the k-thh SfM point visible in image i. The confidence map C, is learnable and downweights unreliable SfM points.
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Structure loss To preserve sharp geometric structures captured by the initial depth maps, MAtCha regularizes surface normals and mean curvature between the deformed charts and their initialization:
Lxtruct
(1- M(O)
Σ 1=0
M M,, M 11,,
(A5)
1=0
where N; and M, are the surface normals and mean curvatures of chart i, while No and M are their counterparts derived from the initial depth maps.
Mutual alignment loss To enforce global coherence, MAtCha encourages neighboring charts to align by minimizing distances between projected overlapping points:
Lalign
min((I thy P3 (7(())11,7), uEV.
(A6)
where Vi is the set of sampled UV coordinates on chart P3 is the projection from 3D space to the UV domain of chart j, and T is the attraction threshold that limits the maximum aligmment distance.
Final objective
The overall optimization combines all three objectives:
C Ltie Astruct + JalignCaligne
(A7)
with Aemmrr 4 and Aalign 5.
Although this alignment stage leverages cross- view projection constraints to yield more coherent scene geometry, its accuracy is ultimately limited by the quality of image correspondences. In regions with sparse or unreliable matches, it often introduces noticeable errors, motivating our plane- aware geometry modeling framework.
B.3 GAUSSIAN SURFELS REFINEMENT IN MATCHA
To further enhance the reconstruction of fine-grained scene structures through diffcrentiable render- ing, MAICha introduces a Gaussian surfel refinement stage based on 2DGS. In this stage, surfels are iteratively optimized using joint loss function that combines photometric consistency with geometric regularization terms.
RGB Loss
The photometric loss is defined as a weighted combination L1 loss and D-SSIM:
Ligh (1 A) ACD-SSIM
(A8)
where À = 0.2.
The regularization loss consists of distortion loss and a depth-normal con- sistency loss, introduced in 2DGS.
Regularization Loss
To prevent surfel drifting and enforce cross-chart consistency, the distortion loss is applied:
La Σ waw/ - -2y1
(A9)
where denotes the intersection depth of the i-th surfel and wi is its blending weight.
To promote alignment of surface orientations across different charts, the depth-normal consistency loss is used:
Cn (1_,
(A10)
where n, is the normal of the surfel and N is the normal derived from the depth gradient.
The overall regularization loss is then defined as:
Lag XaLa An
(A11)
with weight values Aa- 500 and >, 0.25.
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Structure loss
The structure loss follows formulation similar to Eq. A5)
n- Σ 1D, Dill + (I-NN N.) + 4 1=0
Sstruct
Σ
I
M
(A12)
k=0
i=0
where D,. N₁, and M; are rendered from the Gaussian surfels, while D;, N₁, and M, are obtained from the charts.
Total refinement loss The complete optimization objective is given by:
Lotal Ligh + Leeg + Lstruct.
(A13)
During each round of 2DGS training, our method employs the same total loss formulation as MAtCha, but enhances it by computing D;, N,, and M, in the structure loss term using our plane- aware geometry modeling. This integration of more robust geometric constraints leads more accurate and consistent reconstructions.
C MORE TRAINING DETAILS
C.I MERGING GLOBAL 3D PLANES
we merge 2D plane masks from different viewpoints by leveraging the 3D scene point cloud. To this end, we first compute the 3D point cloud corresponding to each 2D plane mask, and then evaluate the similarity between every pair of 3D point cloud sets using the overlap ratio. For computational efficiency, viewpoints at once. However, this projection process introduces two challenges: (1) along each camera ray, all points projected onto the same pixel, which ignores occlusion; (2) when the surface points are insufficiently dense, some pixels may not receive projections from actual surface points but instead from points located behind the surface.
As described in Section 3.2
project the entire 3D scene point cloud into all
Both issues degrade the accuracy of global 3D plane merging. To address them, we first render the depth maps of all viewpoints using Gaussian surfels, and then back-project these depth maps reconstruct the 3D scene point cloud. This ensures that every pixel in each viewpoint is associated with a valid surface point. Moreover, to account for occlusion, when projecting the reconstructed 3D scene point cloud into a given viewpoint, we compute the depth values of the points and require that their relative deviation from the Gaussian-renderee depth values does not exceed 1%.
These steps ensure that our global 3D plane merging process is both accurate and efficient.
C.2 PLANE-AWARE NOVEL VIEW SELECTION
As described in Section 3.3 we formulate the selection of plane-aware novel view camera centers as search problem. The objective is to maximize the coverage of plane points, minimize the distance between the camera and the plane, and encourage the camera viewing direction to align with the corresponding plane normal.
Let the camera center be denoted by c, the look-at point by p (i.e., the center of the 3D plane), and the plane normal by The optimization problem is then defined
= arg max eEC
R(c)) + cos ( p. n) D(c.
(A14)
where C denotes the set of visible voxel centers in the visibility grid, R(c) denotes the ratio of plane points visible from e to the total number of plane points. 0(c, p, n) is the angle between the viewing direction (p- c) and the plane normal and D(c, n) denotes the distance from the camera center to the plane.
In addition to proposed plane-aware novel view selection strategy, incorporate elliptical trajectory around the scene center, following prior work (Wu et al. 2025c), to further enhance view diversity.
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C.3 GEOMETRY-GUIDEE INPAINTING
As shown in Fig. A3 training Gaussian representations with multi-view inconsistent inpainting results leads black shadows in inconsistent regions, phenomenon also observed in Guid- edVD Zhong et al. (2025). To address this issue, GuidedVD constrains the diffusion denoising process preserve observed regions, thereby alleviating inconsistencies near these regions. How- ever, it remains ineffective for large missing areas (see Figs. and A8 and suffers from slow training speed (see Table
In contrast, our approach mitigates multi-view inconsistencies by incorporating scale-accurate ge- ometry guidance throughout the generative pipeline (introduced in Section Within this pipeline, we further introduce a strategy that modulates color supervision based scale-accurate depth to re- duce inconsistencies views by depth projection before training the Gaussian representation. For each region, we primar- ily rely on color supervision from a single view: if the region lies on a 3D plane, we select the view that provides the most complete observation of that plane, ensuring consistent inpainting; for non-planar regions, we select the first view where the region is observed. These operations are exe- cuted in parallel via geometric projection, require only a single pre-prccessing step before Gaussian training, and thus incur minimal computational overhead.
inpainting results. Specifically, we first establish correspondences across
As shown in Fig. A3 producing sharper and cleaner renderings.
approach substantially reduces the impact of multi-view inconsistencies,
Results View1
Con. Results View1 (Ours) Incon. Results View2
Con. Results View2 (Ours)
Figure A3: Training results with inconsistent (Incon.) consistent (Con.) multi-view in- painting. Training Gaussian representations with inconsistent inpainting leads to black shadows in non-consistent regions, while our method significantly reduces such artifacts, yielding sharper and cleaner renderings.
C.4 CHOICE OF GENERATIVE MODELS
For the generative models in our experiments, we primarily employ the inpainting-based diffusion model, See3D (Ma et al., 2025 Notably, framework remains highly effective when integrated with alternative generative priors, such as ViewCrafter (Yu et al. 2024b), as evidenced by the quan- titative and qualitative results in Table A2 and Fig. A4 Beyond these, our model is also compatible with camera-controlled diffusion models like ZeroNVS (Sargent et al. 2024) or Stable Virtual Cam- era (Zhou al. 2025). In these configurations, the input images serve as references, while our selected novel views act conditions. The visible masks can thus keep the visible areas unchanged while leveraging color supervision from viewpoints that observe the global 3D plane most com- pletely. This capability is demonstrated in the single-view testing, as detailed in Appendix A.1
Table A2: Quantitative results of our method integrated with different diffusion models.
Reconstruction
Rendering
Method
CDJ
F-Score t
NC1
PSNR
SSIM
LPIPS.
MAtCha
10.12
60.90
79.33
17.81
0.752
0.228
GenFusion
13.05
41.60
69.33
20.14
0.801
0.258
Difix3D+
13.71
43.11
65.34
19.42
0.779
0.231
GuidedVD
27.87
17.29
61.64
22.51
0,822
0.260
Ours (w. ViewCrafter)
7.95
64.19
81.82
22.08
0.812
0.210
Ours (w. See3D)
6.61
65.14
83.98
23.90
0,836
0.199
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MAtCha
GuidedVD
Ours (with ViewCrafter)
Ours (with See3D)
GT
Figure A4: Qualitative comparison from input views using different diffusion models. G4Splat produces high-quality, smooth geometry reconstruction even when paired with weaker generative prior (i.e. Ours with ViewCrafter), and it continues to effectively suppress Gaussian floaters and reduce blurriness in the rendered results.
C.5 DATASET DETAILS
For each scene, we uniformly sample 100 images. For ScanNet++ (Yeshwanth et al. 2023) and DeepBlending (Hedman et al. 2018), we randomly select 5 images as input views and use the remaining 95 images as test views. For Replica (Straub et al. 2019), we conduct three groups of experiments with 5, 10, and 15 input views to assess the performance under varying levels of view sparsity Table AT In all three Replica experiments, we use the same test views to ensure consistent evaluation set across settings.
C.6 EVALUATION METRICS
Rendering Metrics Following prior work (Guédon al., 2025), we evaluate rendering quality using PSNR, SSIM and LPIPS computed with the VGG network (Simonyan & Zisserman 2014).
Reconstruction Metrics Following prior work (Yu al. 2022 Guédon al. 2025). we evaluate the Chamfer Distance (CD) in em, F-score with threshold of 5cm and Normal Consistency (NC)
for 3D scene reconstruction. These metries are defined in Table
C.7 IMPLEMENTATION DETAILS
We implement our model in PyTorch (Paszke et al. 2019) and conduct all experiments on a single NVIDIA A100 GPU, except for the dense-view reconstruction results in Appendix [A. which are obtained on single NVIDIA 3090 GPU. As described in Section 3.4, after completing the initializa- tion stage, we perform three geometry-guided generative training loops. In each loop. 10 additional views generated by See3D are incorporated into the training set. Both the initialization stage and each loop train the Gaussians for 7000 iterations.
As shown in Table 4 training single scene from Replica room_ 0 with 5 input views using method takes slightly over one hour. In addition, we evaluate a variant with a downsampled num- ber of Gaussians, which further accelerates training while maintaining competitive performance, as reported in the Ours (DS) entry of Table
D FAILURE CASES AND LIMITATIONS
In this section, we present and analyze representative failure cases. As shown in Fig. A5 a), our method is partially limited by the capabilities of current video diffusion models. For example, video diffusion models such as See3D (Ma et al.) 2025) often struggle to ensure that the colors in the completed regions exactly match those of the original scene. This can lead to inconsistencies when training Gaussians with the completed images, resulting in rendering outputs that do not fully align with the visible surrounding areas. Nevertheless, by introducing scale-acces accuratee geometry supervi- sion, our method can achieve precise geometry reconstruction even from inconsistent completions, demonstrated in Fig. A5
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Input Rendering
Geometry
Input
Rendering
Geomeery
Figure A5: Failure cases.
Fig. A5 b) illustrates another limitation: approach struggles with heavily occluded regions, such as the chairs partially blocked by a table. Because the table and the occluded regions the chair are in close proximity, generating reasonable novel camera view of the occluded areas is challenging.
We hypothesize that incorporating object-level prior (Ni et al. 2025 Yang et al. 2025) could help reconstruct such severely occluded regions.
Moreover, our method relies on the plane representation to obtain scale-accurate depth. While the monocular depth estimator used for non-plane regions yields satisfactory results in our experiments, we believe that adopting a more general surface representation, which can naturally model both plane and non-plane regions, could yield more accurate depth, partieularly in non- non-plane areas. Al- though such representation may be less computationally efficient than plane-based approaches, it expected to improve overall reconstruction quality.
E LLM USAGE STATEMENT
The authors acknowledge the use of Large Language Models during the preparation of this manuscript. LLMs were employed solely to improve clarity of expression and to refine grammar throughout the text. All Al-generated suggestions were carefully examined, refined, and validated by the authors. The research contributions, experimental design, data analysis, and scientific con- clusions remain entirely the original work the authors.
MAICha
GenFusion
Difix3D+
GuidedVD
Sea3D
Ours
GT
Figure A6: Qualitative comparison from 5 input views with visibility mask. In the GT images, golden regions indicate areas unobserved across the input views. G4Splat outperforms baselines in both visible and unobserved regions, producing superior geometry with improved smoothness and minimal Gaussian artifacts.
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garden
bicycle
flowers
stumg
treehill
MAtCha
GenFusion
Difix3D+
GuidedVD
See3D
Ours
GT
Figure A7: Qualitative comparison using 9 input views on the Mip-NeRF 360 dataset. We present results for all outdoor scenes in the dataset. Our method produces significantly fewer Gaussian floaters and less blurriness, while recovering higher-quality and smoother geometry. These results demonstrate that our approach also performs robustly on non- non-planar and less structured out- door scenes.
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Table A3: Quantitative results from 9 input views on the Mip-NeRF 360 dataset. G4Splat per- forms strongly not only on indoor, Manhattan-style scenes (bonsai, counter, kitchen, room), but also on outdoor, non-Manhattan, and less structured scenes (bicycle, flowers, garden, stump, treehill), demonstrating its robustness in non-planar environments. Top-3 results are highlighted as the first second and third.
bicycle
bonsai
counter
flowers
garden
kitchen
room
stump
treehill
Mean
PSNR
MArCha
15,56
15.36
15.96
13.66
17.94
18.96
17.16
18.52
14,80
16.44
GenFusion
16.50
17.93
17.96
14.37
18.43
19.76
19.80
19.82
15.79
17.82
Di
15.23
16.66
16.68
13.41
17.54
18.68
17.17
18.08
13.03
16.28
Gu
14.89
17.21
17.60
14.23
18.23
18.00
18.50
19.00
13.36
16.78
See3D
16.08
16.32
16.49
15.80
19.94
19.62
18.23
18.26
11.51
16.92
Ours
18.31
17.97
19.94
15.48
19.18
20.84
20.71
19.53
15.99
18.66
SSIM
MArCha
0.281
0.541
0.549
0.218
0.390
0.705
0.616
0.397
0.381
0.453
GenFusion
0.296
0.586
0.577
0.237
0.339
0.562
0.667
0.422
0.383
0.452
Difix3D+
0.227
0.551
0.497
0.185
0.288
0.530
0.568
0.339
0.313
0.389
GuidedVD
0,274
0.550
0.570
01.21
0.345
0.578
0.646
0.403
0.359
0.438
See3D
0.229
0.513
0.526
0.235
0.416
0.732
0.666
0.388
0.280
0.443
Ours
0.345
0.630
0.612
0.289
0.454
0.725
0.733
0.460
0.386
0.515
LPIPS 4
MAtCha
0.482
0.362
0.339
0.520
0.351
0.241
0.325
0.441
0.443
0.389
GenFusion
0.530
0.384
0.384
0.568
0.429
0.335
0.319
0.493
0.513
0.439
Difix3D+
0.509
0.370
0.378
0.529
0.408
0.311
0.350
0.474
0.501
0.426
GuidedVD
0.548
0.444
0.408
0.576
0.448
0.380
0,646
0.502
0.535
0.499
See3D
0.553
0.441
0.394
0.462
0.369
0.394
0.339
0.538
0.564
0.451
Ours
0.484
0.336
0.330
0.450
0.314
0.242
0.286
0.445
0.457
0.371
Table A4: Evaluation metrics. We show the evaluation metrics with their definitions that we use to measure reconstruction quality. P and pa are the point clouds sampled from the predicted and the ground truth mesh. np is the normal vector at point p.
Metric
Definition
Chamfer Distance (CD)
Arcunared Complettoss
Accuracy
min Ilp p2'12 ep min pe
Completeness
mean p
pp
p"112
F-score
Precision Precision Recall
Precision
mean P
min CP.
p p'11 < 0.05
Recall
min p p 1/1 < 0.05
EP-
Normal Consistency
Normal Avcuuccy N-Nomaal Compleieness
Normal Accuracy
mean
(n 12p)) s.t. p" arg minl Pt
p p'lla
pep
Normal Completeness
mean
Epe (n npp) S.t. arg min
p''l1
PEP
27

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Published as a conference paper at ICLR 2026
MAICha
GenFusion
Difix3+
GuidedVD
See3D
Ours
GT
Figure A8: More qualitative results.
28


Reviews:
[{"review_id": "2u8xbYEVUm", "reviewer": "Reviewer_56rG", "summary": "This paper proposes G4SPLAT, a sparse-view 3DGS reconstruction method that uses planar depth priors and geometry-guided video diffusion refinement. The idea is to extract global 3D planar surfaces from input images, convert them into scale-accurate depth maps, and use these to supervise Gaussian optimization, select novel viewpoints, and constrain generative inpainting. The method iteratively refines the 3D model using inpainted novel views and recomputed plane-aware depth maps. Experiments on Replica, ScanNet++, and DeepBlending claim substantial gains in unobserved regions and single-view/unposed settings. Overall, the pipeline combines MAtCha-style plane-scaled depth, 2DGS, and video diffusion priors, but introduces significant engineering complexity in return for modest conceptual novelty.", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "- Clear motivation: improving geometry for generative 3DGS. The paper correctly identifies geometry accuracy as a limiting factor in generative scene completion and explicitly seeks to address shape-appearance ambiguity. This motivation is grounded in observed weaknesses of recent diffusion-enhanced 3DGS methods.\n- Plane-based scale recovery is well-explained and technically coherent. The method extends plane fits across views using SAM, normal clustering, and RANSAC with multi-view consistency checks, yielding scale-aligned depth even in weakly observed areas. While not new, the pipeline is carefully engineered, and ablations show benefit over raw monocular depth.\n- The approach uses plane-aware visibility masks, plane-driven novel-view planning, and plane-based inpainting weighting to enforce consistency with the goal of stabilizing diffusion-assisted NVS. This produces fewer floaters and sharper planar regions compared to GenFusion / Difix3D+.", "weaknesses": "- **Fundamental Reliance on Planar Structures:** The method's core contribution and primary advantage are fundamentally tied to the Manhattan-world assumption. While effective for artificial environments, this reliance makes the approach far less suitable for organic, non-planar scenes (e.g., natural landscapes, complex statues, foliage). The paper's solution for non-planar regions is to fall back on monocular depth estimation, which is the very technique it criticizes for scale ambiguity.\n- **Heavy Engineering Pipeline** - G4Splat is not a single model but a complex, multi-stage pipeline that glues together numerous off-the-shelf components like MAtCha, MASt3R-SfM, SAM, K-means clustering, RANSAC, a monocular depth estimator, and a video diffusion model. This high complexity makes the system brittle - a failure in any one component could compromise the entire pipeline. \n- The approach inherits a strong geometric scaffold from MAtCha, including scale-aligned depth, plane priors, and reliable surface initialization. This prior stabilizes 3DGS optimization in sparse-view regimes and likely contributes to the improved geometric integrity. In contrast, several baselines (e.g., GenFusion and Difix3D+) do not assume a comparable geometric initialization and instead operate in a more challenging setting where depth must be inferred solely from generative consistency. This makes the comparison somewhat imbalanced, and it becomes difficult to isolate how much of the improvement stems from the proposed refinements versus MAtCha's initialization advantage.\n- The qualitative comparisons in Fig. 4 appear to show holes and structural failures for generative baselines that are not typically reported in their original papers. This behavior is plausible when such models are applied without a metric depth prior or explicit plane constraints, especially under sparse or weakly posed conditions. However, the visual degradation suggests that the baselines may not have been given an equally stabilized geometric starting point. A clearer description of how pose supervision, depth alignment, and initial surfaces were handled for each competing method would help ensure confidence in the reported gaps.\n- All datasets are indoor and relatively structured - no explicit experiments test failure modes (irregular geometry, non-planar scenes, outdoor clutter). Standard view splits of 3, 6, and 9 views are followed in relevant literature (ReconFusion, CAT3D), but are not followed in this paper. \n- A strong baseline in ViewCrafter (TPAMI'25) is missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iq85gU1IwO", "reviewer": "Reviewer_HhvE", "summary": "G4Splat proposes a reconstruction pipeline for sparse-view 3D scene reconstruction that integrates metric-scale geometry estimation (leveraging planar priors) with generative priors from pretrained diffusion/image models. Geometry guidance is injected at multiple stages (depth estimation, visibility masks, view selection, and inpainting) to reduce shape–appearance ambiguities and improve multi-view consistency in novel-view completion. Experiments on Replica, ScanNet++ and DeepBlending report improvements in both geometry and appearance, especially in unobserved regions.", "questions": "Provide quantitative robustness experiments on scenes with few planar structures — how does the depth prior behave and how does it affect final reconstructions? \n\nHow does G4Splat handle scale ambiguity when planar cues are incorrect or scarce? Provide failure cases.\n\nPlease report computational cost and latency for a representative scene, and ablate which component (plane-based depth, guided visibility, diffusion inpainting) provides the largest gain.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Identifies a real limitation in prior generative-prior reconstructions (poor geometry in observed areas and inconsistency in unobserved areas) and supplies a concrete fix via planar metric depth and guided inpainting. \n\nEnd-to-end incorporation of geometry at multiple pipeline points (visibility masks, view selection) is sensible and likely to increase multi-view consistency. \n\nBroad evaluation on several standard datasets and claimed improvements on both geometry and appearance metrics.", "weaknesses": "Reliance on planar priors: in scenes without significant planar structure (e.g., natural outdoor scenes, complex organic interiors), the metric-depth derivation may fail; robustness experiments for such cases are not prominent in the material on the forum page. \n\nIntegration with generative priors can still propagate biases from the generative model (style/appearance biases) - the paper does not analyze or mitigate such biases.\n\nComputational cost: combining geometry estimation, diffusion-based inpainting, and splatting can be expensive; readers would benefit from runtime and resource-use breakdowns and ablations on where the gains come from.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HKFdRItDgn", "reviewer": "Reviewer_jjWR", "summary": "The paper introduces G4Splat, a new method for sparse-view 3D scene reconstruction. Building upon existing work MAtCha Gaussians [1], the paper proposes to leverage estimated 3D planes throughout the entire reconstruction pipeline that additionally leverages a pre-trained video diffusion model as a prior. Making use of the usual existence of planar structures in 3D scenes created by humans, the authors propose to extract per-view 2D planes with SAM in order to merge them into global 3D planes.\nThese can then be used to obtain more accurate depth than from sparse-view SfM approaches like MASt3R-SfM [2] used in [1], and to compute improved visibility masks compared to rendered alpha maps, which are required as inpainting masks when combined with a generative prior.\nThe authors further leverage the 3D planes for a plane-aware selection of novel views for inpainting and finally for selecting supervision signals from the geometry-guided inpainting.\nAn experimental evaluation on ScanNet++, DeepBlending, and Replica shows that G4Splat consistently outperforms all baselines w.r.t. both 3D surface reconstruction and novel view synthesis quality. The paper further includes qualitative results for different numbers of input views and an ablation study w.r.t. the use of the generative prior, the 3D planes, and both combined.\n\n- [1] MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views. CVPR 2025\n- [2] MASt3R-SfM: a Fully-Integrated Solution for Unconstrained Structure-from-Motion. 3DV 2025", "questions": "My suggestions to the authors are already detailed in the weaknesses section but here again concretely:\n- It would be interesting to evaluate the approach on more outdoor scenes with less planar structures like the garden and bike scenes in the MipNeRF360 dataset.\n  - While I do see the value in a method that performs particularly well for indoor scenes, this would be beneficial for the paper to better understand possible limitations.\n- Splitting the quantitative (and possibly qualitative) evaluation into observed and unobserved regions would be very interesting to evaluate the behavior of the individual contributions more precisely.\n- I suggest that the authors evaluate MAtCha + See3D (or is that already the second row, i.e., GP only of the ablation study maybe) instead of 2DGS + See3D. It would be the better comparison as a baseline in qualitative and quantitative evaluations in terms of fairness.\n- It would be interesting to also evaluate G4Splat in combination with different generative priors to see whether the 3D planes can make the use of generative priors more effective irrespective of the particular choice which prior to use.\n- Further open questions are:\n  - Are 3D planes extrapolated to unobserved regions? Does this involve additional assumptions? If so, how does it affect results?\n  - How are surface normals obtained (cf. lines 193ff.)?\n  - Why is the depth from monocular depth estimation and scaling better than from dense reconstruction approaches like MASt3R-SfM or VGGT?\n  - What does \"DS\" stand for in Tab.3?\n  - Could you give details about the PM setting in the ablation?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The paper is well written and mostly easy to follow and understand.\n  - The introduction motivates the topic well and discusses the idea following the Manhattan world assumption and the shortcomings of existing works leveraging generative priors.\n  - The related work section is comprehensive. I found Sec. 2.3. about the plane assumption in reconstruction especially interesting.\n  - The method section first introduces the 3D plane estimation step by step (first 2D and per view, then 3D and global), followed by all applications of the obtained 3D planes throughout the entire optimization pipeline.\n- I appreciate the method and paper story of making use of 3D planes everywhere throughout the 3D reconstruction pipeline, following the Manhattan world assumption.\n  - The step-wise estimation of the 3D planes using SAM in 2D, then merging these with RANSAC, is a reasonable and intuitive approach.\n  - The paper shows how to obtain more accurate depth maps and visibility masks using 3D planes as well as more useful novel view selection for inpainting with generative priors.\n    - Fig. 3 visualizes all of these improvements over naive alternatives well.\n- The quantitative and qualitative comparisons with baselines are overall very convincing.\n  - The proposed method consistently and significantly outperforms all baselines for both 3D surface reconstruction and novel view synthesis.\n  - The authors make sure to include a baseline of 2DGS combined with the video diffusion model that they use (See3D), as this model seems to be quite new and not used by baselines.\n  - The authors choose very recent and strong baselines, representing the current state of the art.\n- The runtime comparison shows that the proposed method is roughly as fast as previous works leveraging generative priors, while obtaining better quality.\n- The ablation study shows that all individual design choices contribute to the overall performance of the method.\n- The authors provide additional convincing qualitative results in form of videos on the anonymous website.\n- The appendix provides more results, detailed background to prior works that this paper leverages, training details, and limitations.", "weaknesses": "- Since the method relies on 3D planes, it seems to be quite tailored for indoor and possibly city-like outdoor scenes, or in other words, scenes that actually consist of enough 3D planes. \n  - I am wondering how this method would perform on other kind of outdoor scenes, e.g., the garden or bike scenes in the MipNeRF360 dataset. Are there still enough planar structures to leverage?\n    - In the limitations section in the appendix, the authors do touch on this topic but mainly for obtaining scale-accurate depth.\n  - The paper, appendix, and website mostly show almost only indoor scenes.\n- The paper would benefit from a more precise evaluation of 3D reconstruction for observed and unobserved regions separately.\n  - It would be very interesting to see quantitatively how much each component improves observed and unobserved regions, respectively.\n- Concern about fairness w.r.t. generative prior:\n  - While the authors do provide a baseline of 2DGS + See3D, i.e., the video generative prior that they use in this paper, would MAtCha + See3D not be the better baseline in terms of fairness?\n    - In Fig.4, MAtCha looks quite accurate, just incomplete, whereas 2DGS + See3D has a lot of artifacts. Is this solely due to the generative prior See3D or also due to 2DGS being worse than MAtCha?\n  - Regarding the use of generative priors, the main paper (related work) would need some more details about which approach uses what kind of generative prior and how does it affect results.\n  - To this end, it would be beneficial for the paper to evaluate their method with the generative prior used by baselines, if that is possible. This would be important in order to precisely attribute the performance gains to a better generative prior or to the other technical contributions, e.g., using the 3D planes.\n- Some lack of clarity:\n  - The need for the background Sec. 3.1 about MAtCha Gaussians became only clear much later in Sec. 3.4 that then states that G4Splat builds on MAtCha as initialization. There is no description of the structure of Sec. 3 (after the Method title), but the paper just goes immediately into the background section after the related work, leaving the reader confused why another \"related work\" section in the method section is needed. Writing one or two sentences about the structure of the Method section and the individual subsections and why they are necessary at the beginning of the Method section would resolve this.\n  - From the main paper, it is unclear what role 3D plane extrapolation plays in the pipeline. Are 3D planes extrapolated to unobserved regions? Does this involve any additional assumptions about the layout of an indoor scene, for example?\n    - Line 74f.: \"planar surfaces allow depth extrapolation: a 3D plane can be reliably estimated from partial depth observations and then extended across the entire surface\" states something along those lines.\n  - In paragraph \"Per-view 2D Plane Extraction\" (lines 193ff.), it is unclear how you obtain the normals.\n  - Is the depth from the monocular depth estimator and scaled using the 3D plane geometry really better than the depth estimated by MASt3R-SfM? If this is the case, why? Are more recent DUSt3R follow-ups like VGGT maybe more performant?\n  - The color supervision description in lines 314f. is quite vague and refers to the appendix. If possible, it would be beneficial to move this information to the main paper to make it more self-contained, as otherwise there remain open questions that require the appendix.\n  - Tab.3 misses to explain what \"DS\" in \"Ours (DS)\" stands for. Actually, I was not able to find that information anywhere in the paper. I assume it uses a distilled video model maybe?\n  - The PM setting in the ablation study is not completely clear to me. Are the planes there only used for initialization and for depth supervision or for what parts of the pipeline exactly?\n- The paper misses a related work and potential baseline: Spurfies [1].\n\n\nReferences:\n- [1] Spurfies: Sparse Surface Reconstruction using Local Geometry Priors. 3DV 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gL32aUNmzP", "reviewer": "Reviewer_3Jys", "summary": "This paper proposes G4SPLAT, a geometry-guided framework for sparse-view 3D Gaussian Splatting reconstruction. Existing generative prior methods often suffer from degraded performance due to insufficient geometric supervision and multi-view inconsistency. G4SPLAT addresses these issues by deriving scale-accurate plane-aware depth maps  that leverage the prevalent planar structures in scenes. These accurate geometric cues are integrated into a geometry-guided generation process to refine visibility mask estimation, guide novel view selection, and improve cross-view consistency during video diffusion model inpainting. Experiments on Replica, ScanNet++, and DeepBlending datasets show that G4SPLAT outperforms state-of-the-art baselines in both geometric accuracy and appearance quality.", "questions": "In constructing the Visibility Grid, how do the choices of voxel size and number of sampling points Q affect visibility accuracy and runtime?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper is clearly written, with well-explained derivations of plane-aware depth maps and the geometry-guided generation process, making G4SPLAT’s contributions and implementation easy to understand.\n- The proposed G4SPLAT takes advantage of plane structures in a scene to figure out depth with the right scale, which gives solid guidance even in areas that don’t have much information in sparse-view setups. Ablation results show that generative priors alone offer little geometric benefit and may even degrade quality, while adding plane-aware geometry modeling greatly improves accuracy and consistency.", "weaknesses": "- The proposed method relies on the presence of significant planar structures in the scene, which may not be applicable in certain complex or natural environments. In scenes lacking prominent planar features, the generated plane-aware depth maps may not provide sufficient geometric cues, potentially affecting the quality of the final 3D reconstruction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)