3D Gaussian Flats: Hybrid 2D/3D Photometric Scene Reconstruction
Authors: Maria Taktasheva
Lily Goli
Alessandro Fiorini
Zhen Li
Daniel Rebain
Andrea Tagliasacchi
Venue: NeurIPS 2025 poster ()
Keywords: gaussian splatting
novel view synthesis
planar reconstruction
Primary area: applications

Abstract: Recent advances in radiance fields and novel view synthesis enable creation of realistic digital twins from photographs. However, current methods struggle with flat, texture-less surfaces, creating uneven and semi-transparent reconstructions, due to an ill-conditioned photometric reconstruction objective. Surface reconstruction methods solve this issue but sacrifice visual quality. We propose a novel hybrid 2D/3D representation that jointly optimizes constrained planar (2D) Gaussians for modeling flat surfaces and freeform (3D) Gaussians for the rest of the scene. Our end-to-end approach dynamically detects and refines planar regions, improving both visual fidelity and geometric accuracy. It achieves state-of-the-art depth estimation on ScanNet++ and ScanNetv2, and excels at mesh extraction without overfitting to a specific camera model, showing its effectiveness in producing high-quality reconstruction of indoor scenes.

3D Gaussian Flats:
Hybrid 2D/3D Photometric Scene Reconstruction
Maria Taktasheva Simon Fraser University
Lily Goli" University of Toronto
Alessandro Fiorini University of Bologna
sarriaatatrashhoa@sff..c
Zhen Simon Fraser University
Daniel Rebain
Andrea Tagliasacchi"
University of British Columbia
Simon Fraser University
University of Toronto
Qurs
PSNR: 27.58
Ground Truth
Ours
2DGS
PSNR: 27.68
PS
27.02
3DGS MCMC
Faux render of 3D Gaussian Flats
3DGS-MCMC
2DGS
Figure 1: Teaser We introduce 3D Gaussian Flats, a hybrid representation of 2D Gaussians on semantically distinct planar surfaces and 3ID Gaussians elsewhere (left). Our method achieves photorealistic quality on par with fully 3D approaches, while improving geometry over surface reconstruction methods (right) e.g. no visible hole in the middle of the 'garden' scene from Mip- NeRF360 [T]
Abstract
Recent advances in radiance fields and novel view synthesis enable creation of realistic digital twins from photographs. However, current methods struggle with flat, texture-less surfaces, creating uneven and semi-transparent reconstructions, due to an ill-conditioned photometric reconstruction objective. Surface reconstruction methods solve this issue but sacrifice visual quality. We propose novel hybrid 2D/3D representation that jointly optimizes constrained planar (2D) Gaussians for modeling flat surfaces and freeform (3D) Gaussians for the rest of the scene. Our end-to-end approach dynamically detects and refines planar regions, improving both visual fidelity and geometric aceuracy. It achieves mate-of-the-ai depth estimation on ScanNet++ and ScanNetv2, and excels at mesh extraction without overfitting to specific camera model, showing its effectiveness in producing high-quality reconstruction of indoor scenes.
Equul Advising
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Recent advances in radiance fields and novel view synthesis have enabled the creation of realistic digital twins from collections of real-world photographs 2 These techniques allow for high- fidelity 3D reconstructions that capture intricate details of real-world scenes, making them invaluable for applications in virtual reality, gaming, cultural heritage preservation, and scientific visualization.
However, when optimizing for novel view synthesis on flat and texture-less surfaces (e.g. walls, ceilings, tables that are prevalent in indoor scenes), current methods struggle in producing faithful 3D reconstruction as the problem is photometrically under-constrained [4] Specifically, modern novel view synthesis frameworks like 6), which optimized via volume rendering, model flat surfaces with low densities, resulting in non-opaque representations of solid surfaces; see the surface of the table in Figure I as an example. Conversely, surface reconstruction methods that assume solid, flat surfaces avoid this limitation [7]. However, they compromise visual quality in favor of a more parsimonious 3D reconstruction; see figure
[ Our core research question is whether these seemingly conflicting objectives could be achieved simultaneously.
Some approaches have attempted to answer this questions by first creating a full 3D representation, and then - post-training detecting planar surfaces to enable 3D planar reconstruction [8] 9]. However, these methods do not leverage planar assumptions during the optimization of the scene representation itself, limiting their effectiveness. through various regularizer losses [10]. However, these losses can be hard to tune, they are only suitable for the portion of the scene that is solid and flat, hindering the reconstruction whenever these assumptions are violated.
Others enforce planar assumptions during training
In contrast to these methods, we propose to look at the problem in an end-to-end fashion, conjoining the process of photometric to the one of planar surface reconstruction. To achieve this, we introduce a hybrid 2D/3D representation, where flat surfaces are modeled with 2D Gaussian splats (7) that confined to 2D planes, while the remaining of the scene is modeled with a classical, and more expressive, 3DGS model [6]. By jointly optimizing planar (2D) and freeform (3D) Gaussians, our approach enables better fitting of the final representation to planar surfaces within the scene. During photometric optimization, our method dynamically detects planar regions, and adaptively grows their extent, resulting in reconstruction that retains high visual quality (as measured by PSNR) compared to classical 3DGS scene, while simultaneously achieving superior geometric accuracy (as measured by depth error).
Our evaluations demonstrate that this hybrid representation achieves state-of-the-ard depth estimation results challenging indoor datasets including the new ScanNet++ dataset which was designed for dense reconstruction tasks using NeRF-based approaches, and the legacy ScanNetv2 dataset with sparser camera views. Our method delivers crisp reconstructed surfaces, while maintaining competitive visual quality compared to fully 3D representations. Beyond novel view synthesis, our approach has application in mesh extraction for planar surfaces, producing high-quality meshes and accurate mesh segmentation results across diverse capture setups (DSLR and iPhone captures), without the overfitting issues that negatively affect previous methods trained on specific camera models.
2 Related Work
Modern neural scene reconstruction methods aim generate high-quality 3D representations from 2D images for applications like novel view synthesis []. Despite significant progress, volumetric approaches struggle accurately reconstruct planar surfaces M.. while surface reconstruction methods fail to recover volumetric effects [I2). Finding an approach that accurately reconstructs planar geometry without compromising the quality of the surrounding scene geometry and appearance is a key challenge.
Representations for differentiable rendering Neural Radiance Field (NeRF) [5] pioneered scene reconstruction with 3D neural representation optimized through differentiable volumetric rendering. 3D Gaussian Splatting (3DGS) [6] overcame NeRF slow training/rendering speed by representing scenes as efficiently rasterizable 3D Gaussians, dramatically accelerating rendering while maintaining quality. The impressive speed-quality balance of 3DGS quickly established it standard approach,

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with recent advancements such as 3DGS-MCMC [3] further enhancing its accessibility by elimi- nating the dependency on SfM initialization. Despite these innovations, volumetric representations still struggle with clean geometry reconstruction in flat and textureless surfaces common in indoor environments, hindering applications like mesh extraction. Our method addresses these challenges through hybrid 2D/3I) Gaussian representation that achieves superior geometric reconstruction while preserving rendering quality.
Surface representations and planar constraints While NeRF [5] and 3DGS [6] employ fully volumetric representations, alternative approaches such as [T] 14 model scenes as solid surfaces.
This philosophy inspired SuGaR [3], to use a regularization term that encourages the Gaussians to align with the surface of the scene, and later 2DGS 77. which uses 2D Gaussian primitives
to reconstruct surfaces outperforming prior surface reconstruction methods [1] work [16] uses 2D Gaussians as in 2DGS, with multi-view depth and normal regularization to improve surface quality, while RaDe-GS [7 enables depth and normal rasterization for 3D Gaussians to support similar regularization. Other works introduced more explicit primitives, including planes [18] 191, optimizable geometry through learnable opacity maps [20], and soup planes for dynamic reconstruction [21]. While these methods excel at representing flat surfaces with clean geometry, they typically sacrifice rendering quality and struggle to model phenomena that are better explained by volumetric effects, rather than surfaces. Some methods enforce planar constraints only as regularization losses, such as Guo et al. [2 that uses Manhattan world assumptions on semantically segmented regions and Chen et al. Although helpful, regularizers can be difficult to tune. Our approach instead explicitly detects and optimizes planes within scene reconstruction, avoiding such issues.
14 [5]. Recent
[23] that enforces plane normal consistency in textureless regions.
3D plane detection and reconstruction Another research direction detects planar surfaces in an initial 3D reconstruction and fits planes only to detected regions, extending single-image plane multi-view settings. PlanarNeRF [26] adds a preddccped MLP branch to NeRF, supervised via ground truth labels or plane detection consistency across frames, but prevents plane MLP gradients from affecting the geometry prediction branch. PlanarRecon [8] reconstructs a sparse feature volume, which is decoded into plane features and clustered. AirPlanes [9] and NeuralPlane (27) build 3D-consistent plane embeddings per 3D point, emphasizing semantic priors for accurate detection. While we also use semantic knowledge, our method jointly detects and optimizes planes alongside scene reconstruction, allowing geometry to benefit from planar constraints. Further, unlike these methods, our approach yields full scene reconstructions suitable for novel view synthesis, vs. coarse surface reconstruction.
detection 24 25
Hybrid representations Recent hybrid 2D-3D approaches have explored planar surface representa- tion. Kim and Lim (28) integrate meshes into 3DGS for indoor scenes, using SAM (29] to detect planar surfaces and represent them with meshes while employing 3D Gaussians for other objects. Zanjani et al. (30) combine SAM segmentation with normal estimation to lift 2D plane descriptors to 3D, clustering the planar Gaussians using tree structure. In contrast, our method offers simpler solution by representing the scene with a mixture of 2D and 3D Gaussians. This design remains fully compatible with the 3DGS rendering pipeline, eliminating the need for complex hybrid mesh handling, or hierarchical tree structures.
3 Method
Given N posed images {Ic} and M planar surfaces [Pp}, each specified by binary image masks {Mp.ec. we aim to reconstruct a hybrid novel view synthesis method that combines classical 3DGS model with a 2D piecewise planar representation of the scene. Our goal is to reconstruct the scene so that the planar surfaces are accurately recovered and compactly represented by 2D Gaussian primitives, while the rest of the scene is modeled with 3D Gaussians, with the key objective of avoiding the artifacts that can typically be seen when using 3D primitives to model planar surfaces; see Figure]]
3

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Input Images
Warm-up training
training
Oulpat Images] Masks
3D Caussians
SFM Init
3D Gaussians
Remder
Planar Gaussians
Planes
Render
Input Masks
pias
Figure 2: Overview Training of our model is split into two parts: warm-up, which 3D Gaussians are trained as in [6] using photometric loss; and planar training, in which 3D Gaussians and planar Gaussians are trained along with the parameters of the planes to which planar Gaussians are locked. Planar training performed in alternating phases, with Gaussian parameters frozen while plane pa- rameters are optimized, and vice versa. Legend: learnable (warm up) learnable (Gaussian phase) learnable (plane phase)
3.1 Hybrid representation
Our representation consists of M planes P=(P,), each characterized by its 3D origin and nor- mal (Op, np). The geometry of each each plane Pp is represented through a set of 2D Gaussians Kx such that, G= gg 1=1
gk = N Ex), NR E ER. Es E R2
(I)
Here, M4 is the center of the k-th Gaussian on the plane Pp. and Σn is the 2D covariance matrix, parametrized with a 2D in-plane rotation R2 and a 2D diagonal scale matrix Sg. The plane-to- to-world transformation matrix is defined as T p= R, o), where R is any rotation matrix that satisfies 2=Rn with being the unit vector along the z-axis the world frame. Thus, the degrees of freedom of planar Gaussians can be mapped to world coordinates through the rigid transformation:
a pw jun: 0; 1], Σn = TT diag Ex, 1) pw
yielding standard 3D Gaussian primitive representation suitable for rendering. The remaining scene geometry is represented by unconstrained 3D Gaussians (-x
g+ (ix, Ex), 14 ERR Σg E 3333
(3)
All Gaussians have view- new-deeendent colors represented as Spherical Harmonics, and opacity as vanilla 3DGS. To reconstruct the scene with our hybrid representation, we need to optimize the degrees of freedom of planes P. 2D planar Gaussians G, and 3D freeform Gaussians G. We begin our optimization with a warm-up stage using only 3D Gaussians (for N=3500 iterations). After that, we begin our planar reconstruction where in each round of optimization we: (i) dynamically initialize plane parameters by robustly fitting planes to the current representation (section 3.2);
(ii) alternate between optimizing plane and Gaussian parameters (section 3.2); (iii) densify representation through a (slightly modified) MCMC densification, due to the challenges of optimizing compact-support functions (section 3.4)
3.2 Plane initialization
For compactness of notation, let us drop our indices, and consider the binary mask M <- MM.p for the p-th planar surface in the c-th view, and denote with ı the function that projects 3D point to the coordinate frame of the n-th image. We start by selecting all the Gaussians (i) whose mean projects into the mask, (ii) that are sufficiently opaque, and (iii) that lie within shell of the expected ray termination the n-th image:
g (gk π (Hx)EM, dR>K, [D(T(R))) dul dol
where the thresholds 0.1 and du, 0005 are hyper-parameters that control this selection process, and where D is the expected ray termination map (i.e. depth map), and d, is the depth of the

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Plane
01
dj
dl
Figure 3: Planar Relocation A freeform Gaussian (teal) gets relocated to the plane to become planar Gaussian (brown), when both its distace to the plane (d_) ) and along (dj) the plane are small.
Gaussians. We then extract candidate plane P by RANSAC optimizatin the point cloud that samples the Gaussians:
(5) where we accept P as a viable plane candidate only whenever the mean inlier residual is lower than The set includes the indexes of Gaussians in I that are inliers of the RANSAC process. We further discard planes that are too small with set Z having a smaller size than 100. Once plane corresponding M has been accepted, all the semantic masks for that plane p are excluded from subsequent RANSAC runs. The plane initialization process is repeated for remaining masks, after each completed round of plane and Gaussian optimization, as described in Section 3.3
P.I = RANSAC(fz ~ g ().
Snapping We then remove the discovered inliers from the set of 3D Gaussians ② + G I, and add them to our set of 2D Gaussians + 2D to become planar by transforming to the local plane coordinates, and set the third component of their means and scales to zero. Further, only rotation about the z z-axis in local plane coordinates is preserved
During the latter operation, we clip 3D Gaussians to
Active set update If the accepted plane P, has an angular distance below a threshold an already existing plane, while its origin o also has a small Euclidean distance to the closest Gaussian center on that plane, we merge the two planes. Otherwise, the plane is added as a new plane to the active set of planes P. In merging, we assign the new plane's 's Gaussians to the previously found one. This allows our optimization to merge planar areas that have only been partially observed in any view.
3.3 Optimization
We optimize our representation by block cookc-cooddinate descent, starting each round of optimization by only optimizing the plane parameters for fixed number of 10 iterations, and then freezing these, and optimizing the Gaussian parameters (both 2D and 3D) for another 100 iterations. This alternation in optimization critical to avoid instability; see an ablation in figure 7 In the first optimization block, within each iteration, the parameters of the p-th plane within the e-th image are optimized by the loss:
arg min og
[T - Tella 1 + Amaskk
|IMe.p -M.plla.
Synoo
Cuak
where MM the predicted plane mask, obtained by rendering the mixture of Gaussians with binarized color (white for planar, and black for 3D), and alpha blended using the original Gaussian opacities during the rasterization. In the second optimization block, we optimize all Gaussian parameters jointly:
arg min G.
Lphato + Amask Lmask + λTv Aucale Excale + Sopacityy
where LTv is the total depth variation regularization from Niemeyer al. [10], Lycale is the scale regularizer and Copacity the opacity regularizer from Kheradmand et al. [13] that vanishes the size of Gaussians that are unconstrained by the photometric loss. Note that planar Gaussians move rigidly during plane optimization (6), and move locally in the plane during Gaussian optimization 77) as only their 2D in-plane parameters optimized.
3.4 Planar relocation
We follow 3DGS-MCMC 13] in our training dynamics. For densification of planes, we rely on relocating low-opacity Gaussians to locations of dense high-opacity Gaussians, as this allows
5

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3DGS
3DGS-MCMC
RaDe-GS
2DGS
PGSR
Ours
GT
RMSEL
MAEL
AbaRell
1,251
1.25Pt
1.25%
PSNR1
LPIPS]
SSIM
Aprimitives
(Spplanar)
3DGS 0
0.44
0.34
0.17
0.71
0.89
0.94
27.09
0.20
0.89
2.43M
3DGS-MCMC
2.43M
RaDe-G8 [7
0.65
0.49
0.26
0.64
0.74
0.77
20.13
0.30
0.82
1.58M
2DGS y
0.39
0.24
0.13
0.82
0.88
0.91
25.56
0.24
0.88
1.54M
Ours
0.27
0.18
0.10
0.88
0.96
0.98
27.01
0.21
0.89
2.43M
(27 (7.88%
Figure 4: Novel View Synthesis Quantitative and qualitative results show significant improvement predicted depth compared to previous work, while maintaining comparable rendering quality to the full 3D representations.
transferring between 3D and 2D/planar Gaussians. However, the number of Gaussians on planes, especially when the plane has weak texture, is usually low, leading to a slow densification rate for planes/ / planar Gaussians. To address this issue, whenever freeform Gaussian projects into the current mask (i²x) M, and it is sufficiently close the currently reconstruction, we stochastically re-locate it to the plane. To measure distance, identify the 2D Gaussian with the smallest Euclidean distance to ūg, and measure its distance in the direction of the plane normal d_, and the one along the plane dij see Figure 3 We stochastically relocate this if both distances are sufficiently small, as expressed by the following Bernoulli distribution:
p B(3,, 8 + d1 [ + d1
where & is the cumulative distribution function of a Gaussian, and σo and σ are hyper- that control the stochastic re-location.
4 Results
We validate our proposed method for scene reconstruction through the novel view synthesis task on common indoor scene datasets, assessing both rendered image and depth quality metrics (sec- tion 4.1) We then show application of our method to mesh extraction for planar surfaces (sec-
tion 4.2 Finally, we validate our design choices through an ablation study on different aspects the
method (section
4.3
We provide implementation details in the supplementary material.

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Metric
3DGS-MCMC
2DGS
Ours
RMSEJ
0.46
0.60
0.40
MAE]
0.37
0.44
0.31
AbsRell
0.19
0.23
0.16
< 1.25
0.61
0.63
0.70
< 1.25
0.87
0.77
< 1.25
0.95
0.83
0.97
PSNRT
20.18
21.44
21.75
LPIPS! SSIM
0.29
0.30
0.27
0.83
0.85
0.86
primitives
500K
809K
500K
(% planar)
(17.6%)
200S
Ohes
Figure 5: Novel View Synthesis on ScanNetv2 - Our method outperforms baselines in image and depth quality on ScanNetv2 despite sparse camera views.
4.1 Novel View Synthesis Figures and
We evaluate our hybrid representation's novel view synthesis common indoor scene reconstruction benchmarks and provide comparisons with both state-of-the-ar fully 3D representations and 2D) surface reconstruction approaches. We show significant improvement in the reconstructed surface geometry while maintaining high visual quality.
Datasets We perform evaluations common indoor scene benchmarks ScanNet+ [31 and ScanNetv2 as they primarily feature indoor scenes with flat textureless surfaces suitable for the task at hand. ScanNet++ provides dense scenes with SfM camera poses and sparse point clouds, designed primarily for 3D reconstruction approaches that follow the NeRF [5] paradigm. Conversely, the legacy version of ScanNet i.e. ScanNetv2 offers sparser views without SfM information. Our method works with or without initial sparse point clouds, enabling reconstruction initialized with sparse SfM point cloud ScanNet++ and experiments with randomly initialized point clouds ScanNetv2. For ScanNet++, we use 11 training scenes with ground truth meshes for depth derivation, utilizing iPhone video streams, sampling every 10th frame for training 2x downsampling and every 8th for testing, We chose the scenes that are diverse in their content and contain various planar surfaces. For ScanNet. we evaluate on scenes with sufficient overlapping views of planar surfaces following the data preparation scheme of (27). The 2D plane masks were generated using PlaneRecNet 25 and propagated through the image sequence with SAMv2 video processor (29]
Baselines We compare against SOTA reconstruction methods, both fully 3D representations and 2D surface reconstruction methods. For 3D representations, we compare with vanilla 3DGS (,, and 3DGS-MCMC [13] as it is more robust version to random initializations, and has higher rendering quality. Within photometric surface reconstruction methods, we compare to 2DGS (7] as a widely used state-of-the-art, well to PGSR []6] and RaDe-GS [17], which more recently report improved depth quality. All methods are trained for 30K iterations.
Metrics We use the common image quality metries PSNR, SSIM and LPIPS for evaluating the rendered RGB. Further, we choose depth as strong indicator for the quality of the reconstructed surface geometry. We provide depth quality metrics by computing the rendered depth the expected ray termination at each pixel. We report RMSE, MAE and average absolute error relative to ground truth depth (AbsRel). Additionally, similar to (33]. The metrics are computed only on the defined portion of the fround-truthy depths. We further report the total number of primitives in our model and the percentage that are planar (and thus can be represented more compactly).
provide depth accuracy percentage different
thresholds
Analysis Quantitative and qualitative results across both datasets show significant improvement in depth accuracy compared to all baselines. Notably, our method achieves comparable image quality to SOTA 3D representations on dense ScanNet++ scenes while surpassing them depth quality, evidenced by sharper geometry reconstruction in qualitative examples. The slight PSNR difference with 3D methods reflects trade-off: our constrained geometry enforces correct structure, while unconstrained methods can inflate PSNR by fitting view-dependent effects with incorrect geometry.
7

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iPhone
D J. Scene
AirPlanes
PlanarRecon
Ours
GT
Render
Meshing
Sesmentarion
Acci
Campl
Precision
Recallt
VOL
Airplanes (
PlanarRecon
Ours
Airplanes
PlantRReon
50.61
Ours
4.60
32.59
18.50
75.00
39.12
0.67
0.23
Figure Mesh Extraction Our method shows consistent results across iPhone and DSLR captures, while baselines typically overfit to one camera type. Qualitatively, our approach extracts complete meshes for most target planes with fewer inaccurate plane detections (shown in gray) compared to baselines. Target planes are shown with distinct colors on the ground truth.
In the sparser ScanNetv2 scenes, our approach delivers superior performance in both depth and image quality, leveraging the planar prior of indoor environments to overcome the geometric ambiguity that challenges pure 3D methods sparse captures. Our method also substantially outperforms 2DGS in both image fidelity and depth accuracy metrics.
4.2 Mesh Extraction figure
Our method enables mesh extraction from reconstructed 3D planar surfaces. For each plane, we un-project all 2D segmentation masks to 3D by computing ray-plane intersections, yielding point cloud. This point cloud is downsampled using fixed-size voxels and rasterized onto plane coordinates to create an occupancy grid. We then use Marching Squares for contour extraction (We omit small contours with less than 100 points). followed by car-clipping triangulation to produce the final mesh. We evaluate the quality of the retrieved mesh for the planar surfaces and compare our method to planar reconstruction methods.
Datasets We ScanNet++ extract planar surface meshes. We show results both on the subset of this dataset captured by iPhone and also the DSLR subset, showing that our method can handle different camera models, while previous methods usually overfit we follow the approach of Watson et al. [9] to obtain a ground truth planar mesh. We then only consider the subset of planes in the ground truth mesh that we have annotated segmentation masks for each scene. We provide details on selecting these planes in Appendix
modality. For ground truth,
Baselines We compare against previous planar reconstruction methods AirPlanes 9 and Pla- narRecon [ that provide extracted planar mesh output of their methods. We follow the same evaluation setting as in the original papers on the iPhone subset of the dataset. For DSLR images, we crop the images to the specified FoV in each baseline to match their training distribution.

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w/o TV loss
W/O mask loss
w/o plane opt.
simult. opt.
W/O snap
reg. w/O snap
W/O relocation
Ours
design
Optimizatioon design
Ganeiian design
model
weo plane qptimization
sinull joint optimizaation
stupping
w/o suapping
PSKRI
26,83
25.24
24,02
21.08
19,52
25.53
21.89
20.00
0.40
Figure 7: Ablation on design choices Loss components and optimization strategy critical, with simultaneous Dlane-Gaussiag optimization causing significant drops. 2D Gaussian snapping greatly improves depth accuracy compared regularization alternatives. Similarly, Gaussian relocation is essential.
Metrics We report mesh accuracy metrics including accuracy, precision, recall, completeness and Chamfer distance as defined in Ye al. 27 We also provide mesh segmentation metrics that evaluate how well detected plane segments match ground truth segments following [
Analysis Our method outperforms the baselines on DSLR images subset of the dataset. Unlike previous methods that are trained on specific modalities (i.e. phone camera) and struggle to transfer to different camera models (i.e. DSLR camera), our approach maintains consistent mesh quality due having zero-shot mesh extraction on test scenes through photometric reconstruction, Additionally, our method outperforms PlanarRecon on iPhone data, while having competitive performance to AirPlanes. Qualitative results reveal that both PlanarRecon and AirPlanes extract extrancous planes with numerous random small fragments, resulting in unsightly and impractical meshes. In contrast, our method produces clean planar surfaces, yielding a more coherent and usable reconstruction.
4.3 Ablation Figure 7
We ablate our design choices and additionally test our method's robustness to random point cloud initialization (in table Tb.
Loss design We ablate the effect of Lmask and Lrv. Although removing these losses reduces the image quality by some margin, it affects depth quality more significantly. Qualitative rendering shows that Lmskk contributes significantly detecting and growing 2D Gaussians.
Optimization design Our method is based on optimizing Gaussians and plane parameters together in alternating fashion. We show that fixing plane parameters with optimization degrades results both quantitatively and qualitatively. Simultancous joint optimization of Gaussians and planes also affects the results negatively. In Figure 7 note how the floor plane gets stuck above the ground level, as revealed by its intersection with the bin.
2D Gaussian design Using hybrid 2D/3D Gaussians is one of the main components of our design. Therefore, we ablate the necessity of having 2D Gaussians by disabling snapping as described in Section 3.2] This shows a significant drop in depth accuracy, which is also evident in qualitative results. As an alternative to snapping, we can regularize the smallest scale component in planar Gaussians. However, we find that this approach is difficult to tune and provides suboptimal results. Finally, we ablate our densification process with relocation of Gaussians to planes. Without relocation. planes are not fully detected, with the planar Gaussians comprising the plane maintaining low opacity. Furthermore, some of the Gaussians remain close to the plane while not being detected belonging to that plane.

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Conclusions
We introduce 3D Gaussian Flats, a hybrid 2D/3D Gaussian representation that accurately models planar surfaces without sacrificing rendering quality. Our method jointly optimizes 2D Gaussians constrained to planar surfaces alongside free-form Gaussians for the remaining scene. By leveraging semantic segmentation masks, predict both full 3D representation and semantically distinet planes for planar mesh extraction in indoor scenes. Our approach achieves state-of-the-art depth estimation indoor scene benchmarks while maintaining high image quality. Additionally, our planar mesh extraction method generalizes across different camera models, overcoming domain gap limitations that typically cause previous methods fail.
Limitations Our reliance on initial 3DGS reconstruction often generates insufficient Gaussians in flat areas with no texture, although this potentially can be addressed via more adaptive densification strategies. Further, using a weak spherical harmonics appearance model still leads building extra geometry to compensate for view-dependent effects, which stronger appearance model would resolve. Additionally, we depend on 2D semantic masks from SAMv2 that may contain errors. but our method will naturally improve alongside advances in semantic segmentation. Finally, our RANSAC-base approach, while robust, introduces computational overhead that extends training time. We believe our hybrid representation opens exciting new avenues for research into more efficient approaches that balance geometric precision with visual fidelity.
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airplanes
g
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Random initialization We analyze the effect of having sparse point cloud initialization versus ran- dom initialization in our method on 11 DSLR scenes from ScanNet++ [31]. for random initialization we do 5000 iterations in our warmup stage, as opposed to the usual 3500. We show that our method A Full mesh extraction results- - Figures 8 to 10
We evaluate our hybrid representation the task of full mesh extraction using the method from 77. we do it in addition to the planar-only mesh extraction experiments presented in Section 4.2 concate- nating the two meshes together and comparing them to common benchmarks from Section 4.1
Datasets We evaluate on + 31 common indoor scene benchmark, as well on subset of suitable indoor/outdoor scenes from ETH3D (34), which provides high quality mesh and is more challenging because of sparse image supervision.
Baselines For ScanNet++ we reuse the models trained iPhone data stream and evaluated the task of NVS in Section 4.1 to access mesh quality reconstruction. On ETH3D, in addition, we evaluate Gaussian Opacity Fields (GOF) [35,, an extention of 2DGS for higher quality mesh reconstruction, and DNSplatter [36]. a method supervising 3DGS with mono-depth
To obtain the mesh, and ours, rather than the expected ray termination as in default settings (i.e., average depth). For PGSR we their proposed unbiased depth computation, and for Gaussian Opacity Fields extract the mesh using the level set of the Gaussians, hence the mesh is not colored.
TSDF fusion with the median depth estimate for 3DGS, 2DGS, DNSplatter
Metrics We use the same metrics as for meshing task in planar mesh experiments Section 4.2 compute the F1-score at cm threshold. For both of the datasets, image.
We
every 8th image test
Analysis We provide full mesh renders along with the metrics on ScanNet++ in Figure g For ETH3D, in addition to mesh renders in Figure [O] provide rendered novel views from the test set in Figure Note that captured planar surfaces are unbiased and outline well the structures of the scenes. Moreover, in the sparse view setting ETH3D dataset we achieve notable rendering quality improvement.
B Additional ablations Tables 1 and 2
maintains the robustness random initialization similar 3DGS-MCME (13), and despite a drop in number of planar Gaussians, it achieves comparable depth and image quality metrics to our method when initialized with SiM sparse point cloud.
Table 1: Ablation on initialization - Our method robust to random initialization and achieves comparable performance to when initialized with SfM point cloud.
Method
PSNR1
SSIM
LPIPS]
RMSEJ
MAET AbsRel] #primitives (splanar)
3DGS-MCMC (SfM)
23.38
0.87
0.24
0.41
0.24
0.26
1.13M
Ours (SIM)
23.42
0.87
0.24
0.20
0.13
0.12
1.13M
(31%) %)
Ours (Random)
23.30
0.86
0.25
0.21
0.14
0.13
1.13M
(21%)
Full metries set for ablation design choices We provide the full set of metrics for ablation design choices (described in section 4.3) in the table
Note that the released codebase for DNSplatter does not support multiple camera models (different camera intrinsics) for aligning mono-depth to SIM points, therefore we cannot easily report the metrics for 'Electro" and "Terrace" scenes.

---

3DGS
2DGS
POSR
Ours
Ground Truth
Acc|
Compl
Chamfer
FIf
3DGS
0.14
0.12
0.1274
0.5639
2DGS
0.27
0.15
0.2082
0.5280
PGSR
0.13
0.15
0.1404
0.5981
Ours
0.25
0.12
0.1833
0.5820
Figure Full Mesh Extraction Results on ScanNet++ Out method achieves competitive performance for surface reconstruction, while mainatining the rendering quality. Checkered surfaces indicate different planes, planes are usually behind the T mesh as they represent unbiased surfaces. Some of the meshes are shown from outside of the indoor scene to highlight the planar alignment.
C Additional video and 3D mesh results
We provide video renderings of RGB and depth for our method compared to baselines in https://theiilab.giinn http:/aa.ggttuuboooooogg.... Video results best capture the significant enhancement of our approach over baselines in depth estimation and accurately modeling scene geometry.

---

3DGS
2DGS
GOF
Ours
GT
Electro
Terrace
Delivery area
Method
PSNR1
LPIPS.
SSIM
PSNR
LPIPS.
SSIM
PSNR1
LPIPSJ.
SSIM
3DGS
16.45
0.38
0.72
20.77
0.27
0.78
19.48
0.29
0.83
2DGS
16.40
0.41
0.72
20.82
0.29
0.79
19.26
0.35
0.81
GOF
17.34
0.36
0.71
20.80
0.27
0.75
19.40
0.33
0.79
PGSR
16.64
0.41
0.69
DNSplatter
19.56
0.24
0.77
Ours 18.72
0.31
0.75
22.57
0.22
0.81
22.56
0.21
0.87
Figure 9; Rendering Results ETH3D Scenes Our method outperforms the baselines in terms of rendering quality on this set of sparse view outdoor/indoor scenes, and the planar representation is crucial for achieving good novel view synthesis in sparse scenarios.
Table 2: Ablation on design choices Loss components and optimization strategy are critical, with simultaneous plans-Ga optimization causing significant drops. 2D Gaussian snapping greadl improves depth accuracy compared to regularization alternatives. Similarly, Gaussian relocation is essential.
PSNR 26.83
LPIPS 0.27
SSIM 0.86
RMSEL 0.25
MAE
AbsRelf 0.09
Full model
0.18
Loss design:
w/o Lrv
23.24
0.34
0.82
0.34
0.24
0.13
w/o Lmusk
24.02
0.32
0.83
0.62
0.53
0.29
Optimization design:
w/o plane optimization
21.08
0.37
0.80
0.54
0.43
0.24
simult. joint optimization
19.52
0.38
0.79
0.40
0.32
0.18
2D Gaussian design:
w/o snapping
25.53
0.31
0.84
0.38
0.31
0.17
reg. w/o snapping w/o relocation
21.69
0.35
0.81
0.36
0.28
0.15
20.00
0.37
0.80
0.59
0.50
0.28
Additional qualitative results
Figures 11 and 12
We provide more qualitative evidence for the performance of our method compared to 2DGS 7 3DGS (6) and 3DGS-MCMC [13] baselines on the ScanNet++ (31 dataset in figure [1] The results show how baselines particularly struggle with reconstructing accurate geometry for the textureless areas while our method significantly improves upon these methods in depth estimation and keeps the visual quality of images.

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3DGS
2DGS
GOF
Ours
Ground Truth
Electro
Terrace
Delivery area
Method
Chamfer
F11
Chamfer
F11
Chamfer
F1
3DGS
0.6524
0,2511
0.3258
0,4517
0.3064
0.2335
2DGS
0.5873
0.2570
0.3312
0,4036
0.3265
0.2366
GOF
0.5371
0.2991
0.2107
0.4045
0,2939
0.3131
PGSR
0,4266
0.4287
DNSplatter
0.2488
0.2516
Ours
0.4062
0.3009
0.1480
0.5033
0.1825
0.3313
Figure 10: baselines.
Full Mesh Extraction Results on ETH3D Scenes - Our method outperforms the
Further, we provide more visualization for our estimated planes on ScanNet++ [31 dataset, showcas- ing the perfect alignment of our planes with the detected planar surfaces figure
E Input planar masks
2D semantic masks Our method relies on input consistent 2D segmentation masks of planar surfaces. To obtain these masks, can either annotate each image collection manually automate the process for larger scenes. To automatically obtain the 2D segmentation masks, we employ PlaneRecNet 25 and SAMv2 video segmentation model 229 create annotation pipeline. We first input images to PlaneRecNet to obtain 2D plane annotations that are not semantically consistent across the image collection. We the plane probability threshold to 0.5. While this method works well on iPhone images, it produces fewer plane annotations for DSLR images, that are out of distribution for its network trained on iPhone data. We then input these unmatched masks as seed SAMv2. In order to do that, we order image collections that are not already sampled from video. We propagate masks from the initial frame 16-frame chunks of the sequence the next 15 frames, and match SAMv2's prediction with any subsequent 2D masks output from [25,, using Hungarian matching with an ToU metric. Although largely effective, this process is prone to error accumulation through mask propagation. However, we assume resultant masks are semantically consistent across the image sequence. We provide sample segmentation of an input sequence in the supplementary video and on the website.

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3DGS
DDGS-MCMC
RaDe-GS
2DGS
PGSR
Ours
Ground Truth
Novel view synthesis and depth Qualitative results on ScanNet+ iPhone dataset show our superior performance both image quality and depth estimation in novel views. Note the limitation of the quality of Gaussian Splatting based methods for textureless surfaces, which makes the plane fitting procedure less robust.
Figure 11:
5

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We provide visualizations of output planes on the rendered views of ScanNet+ DSLR streams (top 2 rows) and iPhone stream (bottom 2 rows). Pink markings are due to the anonymization the original ScanNet++ dataset. While some planar surfaces are missed due to lack of manual 2D planar mask annotation, the captured planes are reconstructed faithfully.
Figure 12:
Masked ground truth meshes For the planar mesh extraction task, we only consider planes with annotated segmentation masks from the ground truth mesh, as the 2D plane segmentation task is orthogonal to our method. To identify the relevant subset of planes, we unproject points from the ground truth depth maps that correspond each plane according its segmentation mask. We then fit plane to each resulting point cloud using RANSAC and compile these fitted planes into a set S. We match planes from the ground truth mesh to those in set by applying two criteria: the normal cosine distance must be less than 0.99 to at least one plane in S, and the distance between their closest points must less than 0.1. Doing this allows for computational efficiency and increased robustness to missing or undersegmented planes in the input 2D annotations.
Code We release our codo publicly for reproducibility purposes and facilitate future research in this area. We base our code on the 3DGS-MCMC paper [13] and additionally use SAMv2 [29]. and PeaneRecNet 25 generate masks. The baselines are evaluated using their official released We further utilize AirPlanes
code 7
9 code to compute meshing metrics.
httpp://gttabb.cc///llaaaaa//a/

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Hyperparameters settings
We Ti and dl as hyper-parameten that control the stochastic re-location. These parameters chosen depending on the metric scale of the dataset, and are defined in millimeters. For both datasets we used _ 0.01 and dj = 0.3. We observe that setting Amask 0.1, yields best results empirically, For regularizers, we use Xyv=0.1. Ascale -0.01 and Augucity = 0.01 following [TO] and [13]. We use same scheduling policy for learning plane origin and normal (rotation) as for the Gaussian means the vanilla 3DGS. All experiments were conducted memory. The method runs for approximately hour for a single ScanNer++/ScanNet22 scene, which is comparable PGSR [1).. the second best method for geometric quality according to experiments and 1.5x longer than EDGS-MCMC [13], the best method for Novel View Synthesis. The training time is increased due the RANSAC overhead and block-coordinate descent optimization of planar parameters. Additionally, mesh extraction takes minutes and SAM mask propagation is on average minutes long, depending the scene type. We believe that the training time can be reduced in future work with addition of customized CUDA kernels.
single A6000 ADA GPU, with 46GB


Reviews:
[{"review_id": "JLPFwGS4Gt", "reviewer": "Reviewer_bJrA", "summary": "This paper introduces a novel method for representing 3D scenes by combining 2D and 3D Gaussians. Specifically, 2D Gaussians are employed for planar surfaces, while 3D Gaussians handle the remaining scene elements. The selection of 2D Gaussians is based on their projection onto pre-estimated plane masks and their adherence to a RANSAC-based plane fitting, allowing the method to automatically differentiate between 2D and 3D Gaussian assignments.", "questions": "- What is the mesh quality compared to 2DGS, Gaussian Opacity Fields and DNSplatter?\n- How is the method different than simply having monodepth/mononormal supervision? \n- What RANSAC did the authors use to get the plane parameters? \n- What is the advantage of using a mix of 2D/3D Gaussians instead of only 2D ones? \n\nI am willing to improve my score, but only if the authors show mesh quality comparisons on standard datasets against 2DGS, Gaussian Opacity Fields and DNSplatter.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "--- Strengths ---\n\nThe concept of integrating both 2D and 3D Gaussians within a single scene representation is interesting. However, the current experimental setup does not sufficiently motivate or justify the advantages of this hybrid approach.\n\n--- Weaknesses ---\n\nThe paper in its current state shows several major weaknesses. \n\n- Limited Justification for Hybrid Approach: The proposed method for identifying 2D Gaussians - relying on projections into pre-estimated plane masks and RANSAC for robustness - lacks a clear advantage over existing techniques. It is unclear how this approach surpasses methods that utilize monocular depth or normal supervision, such as DNSplatter, in terms of geometric accuracy. Furthermore, given that 2DGS has already demonstrated superior geometric accuracy with disc-based representations compared to 3D Gaussians, the rationale for a hybrid approach rather than exclusively using 2D discs is not well-motivated.\n- Insufficient Experimental Validation: The experimental section is very weak, particularly concerning geometric accuracy comparisons. The paper primarily focuses on photometric/depth accuracy and some geometric comparisons with planar segmentation methods, neglecting crucial baselines. Essential comparisons with state-of-the-art 3D Gaussian Splatting (3DGS) methods that prioritize geometric accuracy (e.g., F1 score of the extracted mesh), such as DNSplatter, Gaussian Opacity Fields, and 2DGS, are missing. A table presenting geometric accuracy metrics (mesh extraction quality) across standard datasets like Tanks and Temples, DTU, or ETH3D would significantly strengthen the paper.\n- Incomplete Mesh Extraction Evaluation: The current mesh extraction evaluation is limited to planar surfaces. To provide a comprehensive assessment of geometric accuracy for the entire scene, the authors should apply and evaluate a robust mesh extraction method, potentially leveraging the approach demonstrated in 2DGS, which appears straightforward to implement.", "was_revised": true, "final_justification": "The authors managed to address my concerns in their rebuttal. Thus, I improve my rating.", "rebuttal": "We thank you for acknowledging the novelty of our hybrid 2D+3D Gaussian representation. Below, we clarify the motivation behind our design and provide additional evidence on how our method achieves improved geometric accuracy.\n\n**Limited justification for hybrid approach.**\nOur primary motivation is to achieve both photometric and geometric accuracy in indoor scenes, which often contain large, weakly textured planar surfaces. Optimizing purely for photometric loss and without geometric priors, as done in 3DGS and 2DGS, often fails to capture correct geometry in these areas, particularly with specular or uniform textures like walls (e.g. see DN-Splatter [1] within which the caption of Figure 3 clearly states that “The Gaussian based methods Splatfacto, SuGaR, and 2DGS are trained on only photometric losses and thus severely struggle to capture the scene geometry in low texture environments\").\n\n**Q: Why not use depth (e.g. DN-Splatter) or normal supervision?**\nNote that DN-Splatter assumes depth input from a sensor, not only rendering the problem of 3D reconstruction significantly easier, but limiting applicability with data acquired by a limited class of devices. More specifically, only Apple has continued shipping LiDAR sensors in consumer smartphones, with their top-of-the-line “Pro Max” trim carrying them. Samsung, back in 2019–2020 experimented with rear-facing time‑of‑flight (ToF) sensors on some Galaxy S10 5G, S20, and S20+ models, but these were not Apple‑style LiDAR and Samsung later discontinued them. We argue that investigating techniques that do not rely/require depth input is critical, as it allows a broader consumer base to reconstruct 3D structures with photometric supervision alone.\n\nMonocular depth predictors are a possible alternative, but they can only produce view-inconsistent results, limiting their effectiveness for supervision. For example, the recent Video Depth Anything [2] states how depth consistency in video still presents a challenge, and non-sequential multi-view data (a less structured generalization of video input) is not even considered.\n\n\nIn contrast, our method adds a simple but strong planar prior, requiring minimal user input and leveraging RANSAC to robustly detect and propagate 3D plane assignments. For planar regions, where the geometry is already well-defined, this offers a lightweight alternative tailored to the dominant structures in the indoor scenes.\n\n**Q: Why not use only 2DGS?**\nWhile 2DGS offers improved geometry over 3DGS, it does so at a significant photometric cost. For example, as shown in Figure 4, 2DGS drops PSNR by ~1.7dB, while our hybrid method limits the drop to ~0.2dB, while simultaneously achieving superior depth quality. 3D Gaussians better capture photometric detail and complex geometry, while 2D Gaussians are ideal for modeling planar regions. Our hybrid design combines these strengths, yielding better geometry on flat surfaces without significantly compromising overall image quality. The remaining slight drop in performance wrt. 3DGS (0.2dB) is caused by the limited expressive power of spherical harmonics, as once one imposes a “surface” (vs. volume) reconstruction, complex view-dependent effects cannot any longer be represented by volumetric effects. \n\n**Insufficient experimental validation (i.e. our new experiments).**\nOur experimental evaluation focuses on both photometric accuracy (rendering color error) and geometric accuracy (rendered depth error), which are direct outputs of general-purpose 3D reconstruction pipelines. These metrics are reported on ScanNet++, a dataset well aligned with our goal of accurate indoor scene reconstruction.\nTangentially, we include mesh accuracy comparisons to prior plane-based 3D reconstruction methods, which generate meshes for the planar parts of a scene. However, we acknowledge the value of extracted mesh accuracy comparisons with non-planar baselines as additional evidence, and thank the reviewer for the suggestion.\n\n\nAmong the suggested datasets, only ETH3D features structured planar geometry, while Tanks and Temples and DTU are primarily “object-centric” and less relevant to our use case. DTU consists of small objects that do not contain a sufficient number of planes to make our method beneficial. Tanks and Temples scenes contain not enough scenes of interest as it is mostly focused on complex non-planar geometry.\n\n\nDue to the limited time and compute budget, we provide additional mesh accuracy results on a selection of scenes from ETH3D (using the same mesh extraction strategy as 2DGS). The outdoor scenes were selected to contain parts of the roads and buildings to show the generalization of the method; they are also sparse (<40 train images) and unbounded. The selected indoor scene is similar to the most challenging scene from ScanNet++ we evaluated on in the paper to show generalization on indoor scenes. Note that we cannot evaluate DN-Splatter on ETH3D, as in this dataset the provided depth maps are used to create the ground truth mesh.\n\nOur hybrid method yields competitive geometry results in these scenes, with higher photometric accuracy, further validating the benefit of incorporating planar priors. We leave the fusing of the detected planar geometry into the mesh for future work, but this would simply further improve the mesh quality results.\n\n`Electro` (outdoor) \n|       | PSNR↑ | LPIPS↓ | SSIM↑ | Chamfer↓ | F1↑    |\n|-------|-------|--------|-------|----------|--------|\n| 3DGS  | 16.45 | 0.38   | 0.72  | 0.7282   | 0.0923 |\n| 2DGS  | 16.40 | 0.41   | 0.72  | 0.6156   | 0.2251 |\n| GOF   | 17.34 | 0.36   | 0.71  | 1.4481   | 0.2305 |\n| Ours  | 18.72 | 0.31   | 0.75  | 0.4922   | 0.3170 |\n\n`Terrace` (outdoor)\n|       | PSNR↑ | LPIPS↓ | SSIM↑ | Chamfer↓ | F1↑    |\n|-------|-------|--------|-------|----------|--------|\n| 3DGS  | 20.77 | 0.27   | 0.78  | 0.5778   | 0.0385 |\n| 2DGS  | 20.82 | 0.29   | 0.79  | 0.3411   | 0.3230 |\n| GOF   | 20.80  | 0.27   | 0.75  | 0.2118   | 0.3901 |        \n| Ours  | 22.57 | 0.22   | 0.81  | 0.2795   | 0.4862 |\n\n`Delivery area` (indoor)\n|       | PSNR↑ | LPIPS↓ | SSIM↑ | Chamfer↓ | F1↑    |\n|-------|-------|--------|-------|----------|--------|\n| 3DGS  | 19.48 | 0.29   | 0.83  | 0.5945   | 0.0727 |\n| 2DGS  | 19.26 | 0.35   | 0.81  | 0.3200   | 0.1783 |\n| GOF   | 19.40 | 0.33   | 0.79  | 0.3044   | 0.2740 |\n| Ours  | 22.56 | 0.21   | 0.87  | 0.2305   | 0.3281 |\n \n- `Electro` the most challenging as it contains sparse unbounded views of the cloudy sky, where Gaussian Opacity Fields struggles to extract a correct mesh, while 2DGS mesh extraction uses depth truncation, so it filters out the sky. \n- `Terrace`  the geometry is dominated by a long building in which the default depth truncation parameters for 2DGS and our method cut off the geometry, while Gaussian Opacity Fields mesh extraction extracts that, yet not precisely, while the planar geometry in our method is more precise as evidenced by F1-score.\n- `Delivery area` indoor scene where our method outperforms the benchmarks that use photometric supervision.\n\n\n**Incomplete mesh extraction evaluation.**\nBelow, we follow reviewer’s suggestion and further provide quantitative results on the extracted meshes of ScanNet++ compared to 2DGS, 3DGS, Gaussian Opacity Fields and DN-Splatter.\nWe provide the results for 11 scenes for the methods from the paper and additional results on the one scene for Gaussian Opacity Fields and DN-Splatter due to the limited time and available compute budget. \nWe achieve competitive performance over all baselines, trading off geometric accuracy and rendering quality. Note that DN-Splatter uses sensor depth as input, which drastically improves the quality of mesh, however, at the cost of render quality.\n\n\n`ScanNet++ (11 scenes)`\n\n|              | PSNR↑ | LPIPS↓ | SSIM↑ | Chamfer↓ | F1↑    |\n|--------------|-------|--------|-------|----------|--------|\n| 3DGS         | 27.09 | 0.20   | 0.89  | 0.1972   | 0.3440 |\n| 2DGS         | 25.56 | 0.24   | 0.88  | 0.1725   | 0.4943 |\n| Ours         | 27.01 | 0.21   | 0.89  | 0.1467   | 0.5666 |\n\n\n`ScanNet++ (0a7cc12c0e)`\n\n|                 | Input  | PSNR↑ | LPIPS↓ | SSIM↑ | Chamfer↓ | F1↑    |\n|-----------------|--------|-------|--------|-------|----------|--------|\n| 3DGS            | RGB    | 30.19 | 0.16   | 0.94  | 0.1570   | 0.2810 |\n| 2DGS            | RGB    | 24.19 | 0.27   | 0.89  | 0.1512   | 0.4117 |\n| GOF             | RGB    | 26.98 | 0.20   | 0.92  | 0.1173   | 0.4861 |\n| DN-Splatter     | RGB+D  | 26.73 | 0.10   | 0.92  | 0.0801   | 0.8777 |\n| Ours            | RGB    | 28.16 | 0.20   | 0.93  | 0.1178   | 0.4975 |\n\n\n**Q: What RANSAC did the authors use to get the plane parameters?**\nWe used a custom pytorch implementation of the RANSAC algorithm as described in classical computer vision educational material.\n\n**Bibliography.**\n- [1] Turkulainen, Matias, et al. \"Dn-splatter: Depth and normal priors for gaussian splatting and meshing.\" 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025.\n- [2] Chen, Sili, et al. \"Video depth anything: Consistent depth estimation for super-long videos.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025."}, {"review_id": "sdEZJqC2vW", "reviewer": "Reviewer_T6jG", "summary": "3D Gaussian Flats proposes a hybrid 2D/3D representation that jointly optimizes constrained planar (2D) Gaussians for modeling flat surfaces and freeform (3D) Gaussians for the rest of the scene. This end-to-end approach dynamically detects and refines planar regions within the scene. The core innovation lies in this adaptive, dual-representation strategy, enabling the creation of photorealistic digital twins that are robust to texture-less regions. The performance is supported by extensive experiments on ScanNetV2 and ablation studies.", "questions": "See \"Strengths And Weaknesses\" section.", "limitations": "See \"Strengths And Weaknesses\" section.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n1. It is interesting to segment planar objects from the scene and condition separate Gaussians to model them. This hybrid 2D/3D representation efficiently improve the reconstruction and view synthesis quality.\n3. The paper is well-motivated and the structure is clean and easy to understand.\n\n**Weaknesses** (including questions)\n1. How does the method apply to outdoor scenes that also have planar parts? The author should include more experiments rather than only focusing on indoor scenes.\n2. The qualitative comparison in Figure 1, 4, and 7 demonstrates marginal improvement in terms of visual quality. In addition, the figure seems quite low-res., potentially directly from sceenshots.\n3. It is quite confusing that in Figure 6, 3D Gaussian Flats outperforms Airplanes in terms of “Acc” but inferior in “Comp.” And “Chamfer Distance”. The author should provide more explaination regarding this.", "was_revised": true, "final_justification": "Thanks for the author's response. My concerns have been largely addressed, but I'm keeping my current scores because of the visual quality.", "rebuttal": "Thank you for recognizing our novel hybrid representation and robust 3D plane fitting.\nWe address your concerns below.\n\n**Outdoor scenes.**\nOur method is primarily motivated by scenes containing large, weakly textured planar surfaces, a common failure case for both 2DGS and 3DGS. Such surfaces are especially prevalent in indoor environments, which is why our main evaluation focuses on indoor datasets like ScanNet++.\nTo demonstrate generalization to outdoor settings, we also evaluate two outdoor scenes from ETH3D, which contain roads and building facades with planar geometry. Additionally, our teaser shows the results on Mip-NeRF 360 outdoor scene with rendering quality metrics.\nThese results show that our method remains effective beyond purely indoor environments. Please refer to our response to Reviewer `bJrA` under `Insufficient Experimental Validation (i.e. our new experiments)` for details.\n\n\n**Marginal improvement in visual quality.**\nWe agree that the RGB renderings in Figures 1 and 4 show only modest visual differences a-la glance.\nHowever, these comparisons are best understood alongside their rendered depth maps, which reveal the underlying geometry.\n\n\nIn both figures, methods without our planar prior tend to explain away specular highlights (e.g., the shiny table in Figure 1 and Figure 4) and low-texture regions (walls and floors in Figure 4) using volumetric effects instead of modeling the simple planar geometry paired with a more complex specular appearance or a flat texture.\n\n\nThis incorrect geometry can noticeably degrade rendering quality when viewed from slightly out-of-distribution angles, beyond the well-covered test views, as shown in our supplementary video.\nAs noted in `Line 230` of the paper, our approach strikes a better balance between geometric accuracy and photometric fidelity: we preserve correct planar structure due to our geometric prior while still capturing complex appearance effects such as specularity.\n\n\nWe thank the reviewer for their point about figure resolutions, we will include figures at full resolution in the camera-ready version (note that we have full resolution results in the supplementary).\n\n\n**Mesh accuracy metrics.**\nOur method achieves higher accuracy because the predicted points lie closer to the ground truth surface. However, it has lower completeness since some planar regions (e.g., undetected or unlabeled planes) are missing. In contrast, the Airplanes method covers more surface area, but with less precision. Chamfer Distance reflects both of these aspects, so our conservative predictions which favors correctness over coverage leads to higher Chamfer distance on iPhone scenes."}, {"review_id": "SuMis1ElRf", "reviewer": "Reviewer_BGYx", "summary": "This paper presents 3D Gaussian Flats, a hybrid novel view synthesis approach that proposes using both 3D gaussians and planar-constrained 2D gaussians. This allows it to come close to 3D Gaussian Splatting's rendering quality while improving upon its depth reconstruction in flat areas (ie: avoiding \"holes\" in apparent surfaces) via planar constraints. The method requires image masks for each plane instance (obtained via SAMv2) in addition to camera images/poses as in standard 3DGS. The optimization begins with standard 3D gaussian optimization as in 3DGS before initializing planar surfaces. The planar surfaces are constructed by projecting 3D gaussians onto the image masks, and then running RANSAC based on the gaussian means. 3D Gaussians within a certain range to the plane are then relocated to planar-aligned 2D gaussians. Optimization then alternates between gaussian optimization and the optimization of planar parameters.\n\nThe approach is evalauted on indoor scenes from ScanNet++ and ScanNetv2 against common baselines such as 3DGS, MCMC, and 2DGS. It outperforms all baselines with respect to depth metrics, and is competitve with regards to appearance metrics such as PSNR. Mesh extraction is also evaluated, and the approach outperforms the Airplanes and PlanarRecon baselines.", "questions": "I provisionally feel positive about the paper. The main thing I'd be interested in seeing are even qualitative results on outdoor scenes such as in AV to see if road reconstruction looks better than in 3DGS and/or Tanks and Temples.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Overall the paper writing is clear (although the figures could be enlarged/improved - especially the teaser where I don't think it's clear what \"faux render\" means) and the hybrid approach seems sensible. The results on ScanNet look compelling and the ablations are reasonable.\n\nThe main weakness I see is its reliance on accurate planar segmentation masks and its potential brittleness when the masks are not totally accurate. Furthermore, the evaluation section limits itself to ScanNet scenes whereas a wider evaluation would be interesting (Fig 1 shows a MipNeRF 360 scene but MipNeRF is not evaluated quantitatively). Even if dense ground truth depth is hard to find for most datasets, getting PSNR metrics on M360 for example would give us a sense of how much the hybrid approach degrades visual quality. And even if outdoor scene reconstruction is not a focus of this paper, it would be interesting to evaluate things like road reconstruction on AV datasets like Waymo or KITTI (which also contain sparse depth that could be used for evaluation), or on Tanks and Temples which comes with (slightly imperfect) laser-scan derived meshes.", "was_revised": true, "final_justification": "Thank you for answering my questions. I will maintain my positive rating, although I agree with other reviewers that the experimental section could be strengthened and would strongly recommend adding more baselines to the camera-ready.", "rebuttal": "Thank you for recognizing our contribution in designing the hybrid 2D/3D representation and its ability to produce compelling results on the ScanNet dataset. We will improve the image quality and figure annotations in the camera-ready version.\n\n**Evaluation on outdoor scenes.**\nWe focus on indoor scenes, where large, weakly textured planar surfaces pose challenges for 2DGS and 3DGS methods, making ScanNet a suitable benchmark. However, we agree that demonstrating generalization to other datasets including outdoor settings is valuable.\nAs suggested by Reviewer `bJrA`, we evaluate our method on ETH3D dataset, which includes two outdoor scenes containing planar structures such as roads and building facades. These results show our method remains effective beyond indoor environments. Please refer to our response to Reviewer `bJrA` under `Insufficient Experimental Validation (i.e. our new experiments)` for details.\n\n\n**Reliance on 2D segmentation.**\nWe recognize the reliance of our method on 2D segmentation as a limitation. However, thanks to recent progress in segmentation models (e.g., SAMv2), this requirement is becoming increasingly robust. These models keep the human in the loop through sparse but valuable point prompts, which come at low cost to the user and contribute to more reliable outputs.\n\n\n**Metrics for Mip-NeRF 360?**\nPlease note that the metrics are already reported in the paper within the teaser."}, {"review_id": "EEO9feDGNi", "reviewer": "Reviewer_wJbn", "summary": "This paper proposes a novel method that combines 3D Gaussians and 2D Gaussians binded on the detected planar to represent non-planar and planar areas, respectively. To optimize the hybrid representation, the authors propose a plane initialization method and multi-stage optimization process, which demonstrates helpful in the ablation study. The experiment results indicate the effectiveness of this paper to some extent.", "questions": "Concerns: See Weakness part.\n\nTypos:\n\n1. line 131, wrong ref for section 3.2\n2. line 140, no $d_{th}$", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths:** \n\n1. This paper propose a hybrid representation to leverage the geometric prior of detected planars in the indoor scenes, which improves the quality of depth map estimation. \n2. The experiment results demonstrate this hybrid representation surpasses previous planar reconstruction methods on diverse datasets.\n\n**Weakness:**\n\n1. The major weakness of this paper is the confused application areas described in the introduction and experiment settings. For novel view synthesis, this method shows performance drop compared with 3DGS or 3DGS-MCMC. For surface reconstruction, this method can only reconstruct planar areas, with large missing regions for other areas (as shown in Fig. 6). Thus I concern whether comparing this method with 3DGS(NVS) or PGSR(Surface Rec) is necessary and fair. Fig 4 and Fig 5 only demonstrate this method can obtain better depth estimation results.\n2. For depth estimation, the comparison with 2DGS is also not very fair. The hybrid representation leverages planar prior to refine the depth map of planar regions, while 2DGS relys on no planar consumption and can generalize to ourdoor scenes. The comparison with 2DGS + planar regularizer (such as structnerf) is necessary to demonstrate the effectiveness of the hybrid representation, although the authors say \"regularizers can be difficult to tune\", which is not a straightforward conclusion to me.\n3. Some statements about the relationship between 2D Gaussians, 3D Gaussians and planar are confused in Sec 3. Is each 2D Gaussians binded extractly on the plane for its definition in Eq.(1) and (2)? If so, what's the smallest Euclidean distance of 2D Gaussians in line 182? Should it be always zero? If Sec 3.4 describes how to transform a 3D gaussian near the plane to a 2D Gaussian binded on the plane, I highly recommend do not use \"freeform\" or \"planar\" and change the subsection name, cause desifincation has specific meanings in 3D GS.\n4. In line 147, may M correspond to several planar masks? Because the author say subsequent RANSAC runs after detecting one valid plane.\n5. For experiments, in line 246, the authors mention this method generalize to different camera models well, while previous fails. The reason is unclear. This method relys heavily on the performance of PlaneRecNet. Is this robustness caused by the diverse training data of PlaneRecNet, which contains different types of camera models? Why previous methods fail?", "was_revised": true, "final_justification": "Having reviewed all comments, the authors have addressed most of my concerns. Thus, I raise my rating to BA.", "rebuttal": "We thank the reviewer for recognizing the improved depth and planar mesh quality outputs of our method compared to baselines.\n\n**Confus[ing] application areas.**\nOur method is motivated by the goal of jointly improving photometric and geometric accuracy in indoor scenes, which often feature large, *weakly textured* planar surfaces. Methods such as 3DGS and 2DGS, which rely solely on photometric optimization without explicit geometric priors, struggle to recover accurate geometry in such regions, particularly under specular or low-texture conditions. For example, see Figure 3 in DN-Splatter, which concludes that `The Gaussian based methods Splatfacto, SuGaR, and 2DGS are trained on only photometric losses and thus severely struggle to capture the scene geometry in low texture environments`.\n\n**Q: Performance drop in novel view synthesis (NVS).**\nAs shown in Figures 4 and 5, our method achieves slightly lower RGB rendering quality than 3DGS-MCMC but outperforms 3DGS, while significantly improving depth accuracy. Depth serves as a strong indicator of geometric quality.\n\nThis leads to a key question: does poor geometry affect NVS performance? The answer is yes. Inaccurate geometry can degrade rendering from out-of-distribution viewpoints noticeably (those not well-covered by training views). We show this in our supplementary video. This issue is well-documented in the literature [3, 4], which highlights how weak geometry can lead to rendering artifacts under more aggressive viewpoint shifts from training views, than is usually present in NVS test sets of common datasets.\n\n**Q: Only reconstructs planar areas for surface reconstruction.**\nWhile we explicitly apply geometric priors only to planar regions, we can still extract full-scene meshes using the same strategy as 2DGS. As shown in the `Incomplete Mesh Extraction Evaluation` section (see Response to Reviewer `bJrA`), overall our method produces more accurate meshes than all baselines, demonstrating the benefit of incorporating planar priors. Figure 6 presents planar-only mesh results to *allow for a fair comparison* with prior plane-based reconstruction methods, which typically report metrics solely for planar regions.\n\n**Unfair comparison to 2DGS.**\nThe reviewer raises an important point: incorporating planar priors does significantly improve geometric accuracy, especially in depth estimation. This is indeed a core contribution of our method: accurately detecting and fitting 3D planes, which vanilla 2DGS lacks and thus struggles in planar regions.\n\nImportantly, our hybrid design is not simply combining 2D and 3D Gaussians everywhere. We use 2D Gaussians exclusively in the detected 3D planar regions where geometry is known, and reserve 3D Gaussians for non-planar areas without priors, where their higher expressiveness helps photometric reconstruction. This separation is deliberate: 2D Gaussians are compact and accurate when structure is known, while 3D Gaussians handle unconstrained regions. Further, while we implemented our solution on top of 3DGS (i.e. non-planar splats are 3D gaussians), there is no reason for which what we proposed could be implemented on top of a 2DGS backbone (i.e. non-planar splats would be 2D gaussians).\n\nAs for regularization-based methods like StructNeRF, these are not directly applicable, as with the advent of 3DGS we moved NVS training workloads away from ray-based implementations and towards tile rasterizers. Further, while StructNeRF imposes the planar consistency loss within any superpixel, our technique only activates the planar optimization if a likely planar structure has been detected by RANSAC. While a direct comparison is not possible due to the ray vs. tile implementation, considering the annotations as “candidates” allows for better handling of false positives.\nWe will clarify this in the paper.\n\n**Generalization to different camera models.**\nOur method is not uniquely dependent on PlaneRecNet. PlaneRecNet is used as a more automatic prompt generation alternative for SAMv2 compared to manual prompting. SAMv2 performs the actual segmentation. While PlaneRecNet is limited by its training on ScanNetV2 to one camera model, our generalization comes from the robustness of SAMv2. In fact, the results on ETH3D in this rebuttal use manual prompts via a user-click UI to SAMv2, showing strong performance even without PlaneRecNet.\n\n**Answer to Q3 (Method details).** Each 2D Gaussian is bound exactly to a detected plane, as defined in Eqs. (1) and (2). In line 182, we compute two distances: the residual distance from a 3D Gaussian to the plane (z-axis), and the in-plane (xy) distance to the nearest 2D Gaussian. This helps decide whether to reassign nearby 3D Gaussians without growing the plane indefinitely. We agree that terms like “freeform” and “planar” could be confusing and will revise the subsection title accordingly.\n\n**Answer to Q4 (Method details).** Yes, M corresponds to several planar masks for a particular 3D plane from multiple views. The 2D masks are propagated from one view or multiple user clicks using the SAMv2 video model. \n\n**Bibliography**\n\n- [3] Warburg, Frederik, et al. \"Nerfbusters: Removing ghostly artifacts from casually captured nerfs.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n- [4] Goli, Lily, et al. \"Bayes' rays: Uncertainty quantification for neural radiance fields.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024."}]

Author rebuttal:

Meta-review: After a strong rebuttal, all reviewers provided positive final recommendations (with three borderline accepts and one accept). The AC agrees with the reviewers that the combination of 2D and 3D Gaussian representations is interesting, and that the evaluations sufficiently demonstrate its effectiveness. The AC recommends acceptance.

Please include the additional evaluations in the revision and address the misleading points noted by the reviewers.
Decision: Accept (poster)