PlanarGS: High-Fidelity Indoor 3D Gaussian Splatting Guided by Vision-Language Planar Priors
Authors: Xirui Jin
Renbiao Jin
Boying Li
Danping Zou
Wenxian Yu
Venue: NeurIPS 2025 poster ()
Keywords: 3D reconstruction
planar priors
3D gaussian splatting
Primary area: applications
TL;DR: We generate high-fidelity indoor mesh of 3DGS via supervision of planar and depth priors.

Abstract: Three-dimensional Gaussian Splatting (3DGS) has recently emerged as an efficient representation for novel-view synthesis, achieving impressive visual quality. However, in scenes dominated by large and low-texture regions, common in indoor environments, the photometric loss used to optimize 3DGS yields ambiguous geometry and fails to recover high-fidelity 3D surfaces. To overcome this limitation, we introduce PlanarGS, a 3DGS-based framework tailored for indoor scene reconstruction. Specifically, we design a pipeline for Language-Prompted Planar Priors (LP3) that employs a pretrained vision-language segmentation model and refines its region proposals via cross-view fusion and inspection with geometric priors. 3D Gaussians in our framework are optimized with two additional terms: a planar prior supervision term that enforces planar consistency, and a geometric prior supervision term that steers the Gaussians toward the depth and normal cues. We have conducted extensive experiments on standard indoor benchmarks. The results show that PlanarGS reconstructs accurate and detailed 3D surfaces, consistently outperforming state-of-the-art methods by a large margin. Project page: https://planargs.github.io

PlanarGS: High-Fidelity Indoor 3D Gaussian Splatting Guided by Vision-Language Planar Priors
Xirui Jin"
Renbiao Jin" Shanghai Jiao Tong University
Shanghai Jiao Tong University
Boying Li Monash University boying. 1i @ieemooashhcomm edu
Danping Zou'
Wenxian Yu Shanghai Jiao Tong University
Shanghai Jiao Tong University dpzou@sjtu. edu. cn
Abstract
Three-dimensional Gaussian Splatting (3DGS) has recently emerged effi- cient representation for novel-view synthesis, achieving impressive visual quality. However, in scenes dominated by large and low-texture regions, common in in- door environments, the photometric loss used to optimize 3DGS yields ambiguous geometry and fails to recover high-fidelity 3D surfaces. To overcome this limita- tion, we introduce PlanarGS, a 3DGS -bsed framework tailored for indoor scene reconstruction. Specifically, we design pipeline for Language- Language-Prompter Planar Priors (LP3) that employs pretrained languan segmentation model and refines its region proposals via cross-view fusion and inspection with geometric priors. 3D Gaussians in our framework are optimized with two additional terms: planar prior supervision term that enforces planar consistency, and geometric prior supervision term that steers the Gaussians toward the depth and normal cues. We have conducted extensive experiments on standard indoor benchmarks. The results show that PlanarGS reconstructs accurate and detailed 3D surfaces, con- sistently outperforming state-of-the-art methods by large margin. Project page: https://planarg:: gi github...
Introduction
Image-based 3D modeling is long-standing challenge in the computer vision community, aiming to recover the 3D scene from collection of images [42]. serves as a fundamental component for a wide range of applications, including computer graphics, nugmented/virtuss reality (AR/VR), and robotics. Recently, 3D Gaussian Splatting (3DGS) [8] has emerged as a promising approach to modeling the 3D world. Given pictures captured from different viewpoints, 3DGS optimizes both the appearance and the geometry (scale, rotation, and position) of 3D Gaussian spheres to enforce the rendered image to be as close as possible to the real image captured in the target viewpoint. The appearance similarity, described by photometric loss, plays in key role in the 3DGS training process.
Despite its impressive visual quality of view rendering. 3DGS still struggles reconstruct high- fidelity 3D surface model in scenes where large low-texture regions exist, Such kind of scenes are commonly seen in indoor environments. Lack of textures makes using the appearance supervision signal alone difficult to resolve the geometric ambiguities, resulting in inaccurate or false geometric reconstruction. To resolve such ambiguity, the recent method PGSR 4 applies the multi-view geometric consistency constraint to enhance the smoothness in areas with weak textures.
as the
"Equal contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Guueian
Photomeric Constaint
Supervision
Depth
Dapth
TSDF
Plase guided
Cusssian
Co pazzacity
Mati viee Imane
Recomiraction
Pipeline Language Prompted
VI-IM
Inspection
rell, doer, takle,
Geometric Prior Superviston
Nontal Condraint
MV TM
Depti
Doyth Conistency
Price Dopth Corstraint
Figure PlanarGS overview. Our method takes multi-view images and language prompts as inputs, getting planar priors through the pipeline for Language-Prompted Planar Priors (LP3). Our planar prior supervision includes plane-guided initialization, Gaussian flattening, and the co-planarity constraint, accompanied by geometric prior supervision. Both foundation models in the figure are pretrained.
Other approaches 6 46 12 have incorporated monocular depth normal priors in the optimization process. However, existing methods still cannot handle large and texture-less planar regions. Those methods tend to produce locally smooth and globally curvy surfaces instead of perfect planes for those planar regions. One feasible solution to explicitly detect those planar regions and enforce the planar geometry on these regions. This idea has been explored in monocular depth estimation 22 61 36]
and Neural Radiance Field (NcRF) [3]. Those methods, however, highly rely on the performance of heuristic plane detector [22] standalone neural plane detector [13] trained from limited data. However, such kinds of plane detectors often produce noisy segmentation and are difficult to apply to novel scenes (e.g, unseen planar objects not labeled in the training dataset).
In this paper, we introduce novel framework of 3D Gaussian Splatting, PlanarGS, which leverages pretrained foundation models to overcome the aforementioned challenges of indoor 3D reconstruction. Foundation models, typically trained on vast, diverse datasets, exhibit greater generalization and better performance than smaller, task-specific networks. To detect the planar regions in the image, we employ a pretrained vision-language segmentation model (39) prompted with terms such as "wall". "door". and "floor". Using word prompts enables flexible adaptation to new scenes by simply swapping or adding keywords, so the model can identify novel planar surfaces. For instance, in the classroom, might include the extra word "blackboard" the prompt capture the novel planar structures not found in a home environment.
The detection results by the vision- language segmentation model always contain misleading region proposals, so we design the pipeline Language-Prone Prompt Planar Priors (LP3) to obtain reliable planar priors. For example, it is common that two connected perpendicular walls are merged into single planar region. To resolve such ambiguities, additional geometric cues are necessary. Moreover, per-image detection frequently omits certain planes because it relies solely on local image information. Incorporating multi-view context can recover these missing labels by aggregating evidence from adjacent frames. Accordingly, model, to extract the depth and normal maps from neighboring images. Those maps serve as both the extra cues for more accurate planar region identification and the geometric priors guide the 3DGS optimization. We opt for DUSt3R's multi-viee approach rather than a monocular alternative such [56] [57] because produces niwscconsistent 3D structures, enabling robust fusion of region proposals across views and yielding stronger 3D priors for the optimization process.
employ DUS(3R (50), pretrained multi-view 3D foundation
To incorporate planar priors into detected planar regions, we introduce planar supervision scheme during 3DGS optimization. This includes cane-guided initialization, Gaussian flattening, and co- planarity constraint, which together help preserve global surface flatness and suppress local artifacts.

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Depth and normal priors from the pretrained 3D model are applied to under-constrained regions. We impose a prior normal constraint for planar regions, encouraging the surface normals extracted from 3D Gaussians to align with the per-pixel normals predicted by DUSt3R, For low-texture regions, we constrain the rendered depth to match the scale-and-shift corrected prior depth and enforce consistency between the predicted depth and normal. Finally, to compensate for insufficient SfM points in texture-less planes, we densely initialize Gaussians by randomly sampling pixels within each detected plane and using their 3D coordinates, which are back-projected from the depth priors, as initial Gaussian centers.
Our main contributions include:
We introduce novel 3DGS framework that integrates semantic planar priors and multi-view depth priors to achieve high-fidelity reconstruction in challenging indoor scenes.
We design robust pipeline for Language-Prompter Planar Priors (LP3) that applies vision- language foundation models with cross-view fusion and geometric inspection to obtain reliable planar priors.
Our method achieves state-of-the-ee performance on high- figh-fidelity surface reconstruction tasks across the Replica [4), ScanNet++ [60], and MuSHRoom [40 datasets.
2 Related Work
Image-based 3D Modeling: Image-based 3D modeling has been a long-standing goal in computer vision. The typical pipeline is as follows. Firstly, conttture-rrommooee camera poses and sparse 3D points by matching feature points across different views. Then, based generates dense depth maps from multi-view images via dense image matching, which are then converted to dense point clouds. Finally, surface reconstruction [34 17 is applied to recover the 3D mesh and its texture from the point clouds, yielding the final 3D representation of the scene. This pipeline, successfully applied to outdoor photogrammetry for decades, still struggles to reconstruct accurate 3D models in texture-less scenes, such as indoor environments that usually contain large, texture-less planar regions. Moreover, using textured meshes for 3D scene representation often exhibits holes other artifacts when rendered from novel viewpoints. Recently, implicit and point-based representations such as Neural Radiance Fields (NeRF) and Gaussian Splatting have emerged, offering superior expressiveness for jointly modeling geometry and appearance.
(SIM) [42] is used to recover
on the posed images, Multi-view stereo (MVS) 22
43 58 32
Neural Radiance Field (NeRF): Neural Radiance Field (NeRF) 33 utilizes multilayer per- ceptron (MLP) to model a 3D scene as a continuous 5D radiance field optimized via volumetric rendering of posed images. However, because NeRF's training also relies on photometric cues, still struggles to recover accurate geometry in texture-less regions. To address this limitation, existing approaches have explored incorporating various priors. Many of them [48] 62 45 35 use data-driven monocular depth or normal prediction as priors to improve the quality of the results. Other approaches incorporate indoor structural cues into reconstruction. For example, Manhattan-SDF (13) enforces a Manhattan world assumption [T] by constraining floors and walls to axis- axis-aligned planes, and another method (70) relaxes the constraints to the Atlantaworld assumption. Although better results have been achieved, their methods depend on a standalone plane detector trained on limited datasets, which reduces the flexibility across diverse scenes. Moreover, Manhattan-SDD requires an additional MLP to fuse noisy plane segmentations across views by comparing predicted labels from this MLP observations. It is, however, not easy to extend such MLP fusion strategies Gaussian Splatting, which represents scenes purely with spherical Gaussians.
Gaussian Splatting: Comparing with NeRF, 3D Gaussian Splatting [18] represents scenes explicitly with 3D Gaussians, which more flexible and interpretable, as well as achieves significantly faster training and rendering speed. Similar to NeRF, 3DGS also depends on photometric loss for training. It therefore faces the same challenge in the texture-less environments. To enhance geometry reconstruction in Gaussian Splatting, numerous approaches 26 46, 6 12] also utilize monocular depth or normal priors for optimization. PGSR 4 introduces multi-view consistency, and FDS 7 leverages optical flow priors, both demonstrating performance gains. In addition, structure cues of indoor scenes have been exploited to guide Gaussian shapes and densification [T 66 27 Some methods jointly optimize Gaussian reconstruction with other representations, which are effective but
3

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cumbersome. For example, GaussianRoom [33 combines with neural SDF [49] while other works incorporate textured mesh [16 or semantic maps [52 38
However, the aforementioned methods either focus on constraints of local regions or lack effective optimization mechanisms, making it difficult to reconstruct globally smooth surfaces. Our method designs a pipeline that adapts foundation models to obtain accurate planar priors, allowing for the holistic enforcement of co-planarity constraints in 3DGS. Meanwhile, multi-view depth priors also provide geometric scale information for PlanarGS.
3
Method
The whole pipeline of PlanarGS illustrated in Fig. I Firstly, we apply pretrained multi-view -ietw 3D foundation model [50] to obtain depth and normal priors. Subsequently, We leverage pretrained vision-language foundation model (39 the input images. Planar prior masks are generated from the model's outputs through our LP3 pipeline (Sec. S.IPiipiene for Language-Prompted
Planar Priors), which implements rres-vieww fusion and geometric verification using the depth and normal priors. In addition, the depth and normal priors serve to guide 3DGS optimization (Sec. 3.4 Geometric Prior Supervision). Notably, we propose planar supervision mechanism (Sec. 3.. Planar Prior Supervision) that flattens Gaussians and utilizes the planar priors to guide initialization and enforce the co-planarity constraint during optimization. Finally, the figh-fidelity 3D mesh can be recovered from the rendered depth maps from PlanarGS via TSDF fusion B1 34)
3.1 Preliminary: 3D Gaussian Splatting
Fiiree-Dimnnsionll Gaussian Splatting [8] represents scenes with set of Gaussians {G.} initialized by the sparse SIM result. Each Gaussian is defined by the center μe; and a full 3D covariance matrix M; that can be factorized into a scaling matrix S, and a rotation matrix Rd
(x) e (()=-)))> 2, ((=-p:)
Σ, R.S.S R
To project 3D Gaussians to 2D for rendering, the center and the covariance matrix of Gaussian G; can be projected to 2D coordinates 71:
1 KW p4 1|"
Σ = JW=
where W is the viewing transformation matrix and J is the Jacobian of the affine approximation of the projective transformation. Rendering color C' of a pixel can be given by a-blending [20]:
i-1
Ò= E.ETae
T
II
ag)):
KEN
where e; and a; represents color and density of the Gaussian and N is the number of Gaussians the ray passes through.
3.2 Pipeline for Language-Prompter Planar Priors
primarily design small and dedicated networks, which rely heavily on extensive annotated datasets and suffer from inaccurate segmentation.
Existing plane segmentation methods 28
53
68
65
54
51
While they can be applied for coarse plane extraction and reconstruction, their results cannot serve as planar priors that impose strong constraints for high-quality 3D reconstruction, ZeroPlane (29 in Fig. 5 By comparison, our pipeline for Language-Prompted Planar Priors (abbreviated as LP3) employs a vision-language foundation model GroundedSAM [39], applying cross-view fusion and geometric verification to its segmentation outputs to generate planar priors, as in Fig. - Simultane- ously, since 3DGS is trained specifically for every scene, the tunability of prompts allows PlanarGS to achieve better reconstruction in atypical scenes.
Cross-view Fusion: We first provide text prompts the object detection foundation model [30 and get bounding boxes. However, we found that large planes often go undetected in single images because their regions either span beyond the frame or lie close to the image boundaries. As shown in Fig. 2 a), to address the plane omission problem, we utilize the multi-view consistency from neighboring viewpoints for the cross-view fusion of plane boxes. Specifically, we leverage prior depth maps D, (see Sec. 3.4) to back-project the main plane mask pixels p, from neighboring frame (source frame) into 3D points P.:
P. Dr(p.) K K-1 ''.
(4)

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Veighhoring
ev-fasion
(1) Cross-view
Prior Normal
norm-inspect
norm-inspect
(b) Normal Inspection
Price Plane Distance
Detected Edge
wio. edge-inspect
wi edge inspect
(c) Edge Inspection
Figure 2: Pipeline for Language- Pronppee Planar Priors (LP3). (a) We employ cross-view fusion supplement bounding box proposals. (b)(c) Prior normal and plane-distance maps are incorporated for geometric inspection. Consequently, obtain abundant and accurate planar priors from this robust pipeline.
where K is the intrinsic matrix, P. [x. =| is in the camera coordinate system of source frame, P. [u, v, 11" is the homogeneous coordinate of the normalized pixel p, in the source camera. P. is then transformed into the camera coordinate system of the current frame (target frame) Pe and finally re-projected to 2D pixels pr:
P. R2 R.R. P. (t,tR,R,,,,,)
R,
2.P. K.P.P
(5)
After filtering out nested boxes with the same labels, we obtain refined detection results for down- stream segmentation.
Geometric Inspection: Subsequently, we generate pixel-scale segmentation masks through the foundation model [19] according the detected boxes. Nevertheless, sometimes the segmentation masks may incorrectly cover multiple planes together due to imprecise boxes, so we employ inspection from geometric priors shown in Fig. (C). We first convert depth priors D, into surface-norma priors Når using the local plane assumption [37.4] For each pixel in the depth map, we sample its four neighboring pixels and project these pixels into 3D points as Eq. then the surface normal of the local plane at pixel p is:
P1 Po)
P3 1 P2)
(6) We then express the distance from the local plane the camera as (p) P. Nar(p) and get prior plane-distad -lane-distance map 5,.. We apply the K-means clustering to partition the prior normal map Na to separate non- non-parallel planes. For the separation of parallel planes, we identify the outliers of the plane-distance map as geometric edges that violate the local plane assumption. This stage also splits filter them out.
Nar (p) (P1 Po) (P3 P2)
non-planar areas into fragments
3.3 Planar Prior Supervision
Inspired by previous works [6] 22, 21 23). we leverage semantic planar priors as a holistic super- that incorporate semantic features, our method treats the planar prior generation pre-processing stage (see Sec. offering a simpler solution.
vision for indoor reconstruction. Unlike existing approaches [13, 70 64
The Gaussian initialization based on SfM relies feature extraction, leading point cloud deficiency in low-texture regions. We back-project pixels of plane areas to dense 3D points P,, Eq. while obtaining the plane labels of existing Gaussian points by
Plane-guided Initialization:
5

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projecting them to 2D. For each plane region in each viewpoint, we calculate the average distance of existing Gaussians, deciding whether supplement it with dense 3D plane points Pm.
Gaussian Flattening and Depth Rendering: In geometric reconstruction, flattening the Gaussians into planes enables them to conform more closely to the real-world surfaces, meanwhile producing Accordingly, we minimize the minimum scale S and define the minimum scale factor as the normal of the Gaussian n, The normal map of the current viewpoint can be rendered through a-blending with the rotation matrix from the camera to the global world R.:
less ambiguous depth and normal renderings
factor S; = diag(8; 8;, 52, 83) for each Gaussian
L = 1lmin $2, $1)1l1
N R n,a II (1- iEN
For each flattened Gaussian, the plane distance d; to the camera center O₂ can be expressed by projecting the distance of the Gaussian center 14; to the normal direction n,, The rendered plane- distance map 8 also obtained through a-blending:
d: (R (p: -c))
8 8 8= d,a dda
II a az).
(8)
Following method [4] obtain the rendered depth map D of Gaussians from the rendered plane- distance 8 and normal map N:
8(p)
D(p) N
(9)
where p = [u, 6)"' is the pixel position and p is the homogeneous coordinate of
Co-planarity Constraint: To incorporate the holistic planar priors into Gaussian optimization, we
apply the co-planarity constraint on the depth map. We first back-project the rendered depth map D dense 3D points Eq. 4 Following methods 61 22) we define the plane Pm the 3D space as A P = 1, where Am is the plane parameter and its direction represents the normal of the plane. As result, the dense 3D points within the plane region pm should follow an equation Qr, Y and A,, can be obtained by solving the least squares problem:
Am = (2T + +E)
(10)
where Q, P2.. Y, [1, I...1'" and is a small scale to ensure numerical stability with an identity matrix E. Subsequently, we obtain the planar depth map Dp as:
Dy(p) (A K 1)
(11)
The depth D, obtained from plane fitting is then used as co-planarity constraint to the rendered depth. The loss function is defined as:
Lp Np pEp
Σ1DP(() - -(p))la,
(12)
where p represents all plane regions and Nº represents the number of pixels in plane regions. Curved objects and clutter in the scenes are effectively excluded by our planar masks. Since these objects usually exhibit rich textures and geometric details, their reconstruction is less problematic.
3.4 Geometric Prior Supervision
Previous methods mostly introduce depth priors from monocular depth estimation, which suffer from local misalignments [67 [4] 24 25). We instead utilize the pretrained multi-view foundation model [50) and provide view-consistent geometric supervision of Gaussian optimization together with the planar priors.
We align the resized dense depth map Daense obtained from DUSt3R with the sparse depth Deparse derived by projecting the STM-reconstructed point cloud the current viewpoint. Because of the memory consumption, the depth maps are generated in groups, and each group shares the same scale and shift parameters.
Prior Depth Constraint:
s" t"
arg mii s.t
Σ ([Dapasee(P) (p) (s Deense (p) + ttll..
(13)
PE Dppree

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and we utilize the aligned dense depth map as depth prior: D. s" Ddense + t. We leverage the prior depth as constraint on the rendered depth:
Lrd
Σ Moo 1ID, D(p)ll pelt
(14)
Nu
where Moof is the mask from the confidence map of DUSI3R for its depth prediction. The It represents the collection of pixels in low-texture regions, generated by computing a mask through the Canny edge detector 3 on RGB input to mitigate the effect of depth priors' low resolution.
The prior depth constraint serves scale-based supervision, while normal supervision offers greater flexibility in certain scenes and has been widely adopted in recent studies 4 12 Following Eq. and Eq. 6 we can obtain the surface-normal map Na from the rendered depth D. We constrain the rendered surface-normal Na with the prior surface-normal Nar:
Prior Normal Constraint:
Lrn Nar Nall1 + (1 - Nar No) (p E p).
(15)
The normal prior is exclusively utilized on the plane region p we extracted, optimizing the position of Gaussians together with the co- co-planarity constraint in Sec. 3.3
Depth Normal Consistency: Additionally, inspired by works normal consistent regularization between rendered GS normal N and rendered surfcce-nrmea Na to optimize the rotation of Gaussians consistent with their positions:
we introduce the depth
1 Σ
Ldn
(1)((p) N()]ll,,
(16)
Nu
PEtt
where It the same as Eq. 14 to avoid edge areas that violate the local plane assumption in Eq.
3.5 Optimization
The overall loss function of PlanarGS that combines planar and geometric supervision is: Liotal LAGB + Lg A Lan + AzLL + AgL + Aq
(17)
where A are parameters and LRGB includes L1 and D-SSIM loss as 3DGS.
Replica
ScanNatt-
MoST
3DXGS
2DXIS
POINR
DN-Xplatter
Ours
Ours(colored mesh)
Figure Qualitative comparison. We present reconstructed meshes from other methods and PlanarGS. The right column shows colored meshes. The results demonstrate that PlanarGS achieves more accurate and comprehensive high-fidelity mesh reconstruction.

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Experiments
We demonstrate the superior performance of PlanarGS 3D reconstruction of indoor scenes Sec. and conduct ablation studies in Sec. 4.2
Following other works 4 on Gaussian reconstruction in indoor scenes, we conduct our experimental evaluation three indoor scene datasets, including 8 scenes from the synthetic
Datasets:
dataset Replica 4 and 9 scenes from the real-world datasets: scenes from ScanNet++ [60] and complex scenes from MuSHRoom [40].
We use COLMAP 42 for all these scenes to generate
sparse point cloud as initialization.
Implementation Details:
All experiments in this paper conducted the Nvidia RTX 3090 GPU. The training iterations for scenes are set to 30,000, and A1, A2, Àg. A are set as 0.05, 0.5, 0.05, 0.2, respectively. We use "wall, floor, door, screen, window, ceiling, table" as prompts for planar prior generation on Replica and ScanNet++ datasets, as these classes are commonly found in various indoor environments, and additionally add "blackboard, sofa" on the MuSHRoom dataset.
38 to evaluate the quality of reconstructed surfaces, report Accuracy (Acc), Completion (Comp), and Chamfer Distance (CD) in cm. F-score (F1) with a Scm threshold and Normal Consistency (NC) are reported as percentages. For novel view synthesis (NVS), follow standard PSNR, SSIM, and LPIPS metries for rendered images.
Evaluation Metrics: Consistent with existing methods 46
4.1 Reconstruction Results
We conduct comparisons with the baseline 3DGS 18] and Gaussian surface reconstruction methods 2DGS [15), GOF [63], PGSR [4]. QGS [69], DN-Splatter [46). For 3DGS, we obtain depth maps by applying alpha-blending integration the z-coordinates of the Gaussians and generate mesh through TSDF. For DN-Splatter, we apply depth priors from the multi-view foundation model that
same as ours.
The quantitative comparison on three datasets are presented in Tab. [ aand Tab. 2 The reconstruction results of our method significantly outperform those of other methods across benchmarks, while simultaneously ensuring high-quality novel view synthesis in Tab. [ Additionally, PlanarGS requires training time within one hour, comparable other 3DGS- DDGS-based methods. We also generate meshes directly from the depth priors provided by DUSI3R using TSDF, as shown Tab. to demonstrate the high-fidelity advantage Gaussian splatting methods.
Quantitative Results:
Table 1: Quantitative results of surface reconstruction and NVS MuSHRoom dataset. Our method outperforms other methods on most metrics. The best results are marked in bold.
Surface
Reconstruction
NVS SSIM
Acc|/
Comp.
CD4
F11
NC1
PSNR
LPIPS
3DGS
18
12.01
11.85
11.92
38.53
62.00
25.79
0.8775
0.2059
2DGS
15
9.16
10.27
9.71
51.50
73.65
25.67
0.8744
0.2265
GOF
15.65
8.50
12.07
42.57
61.47
24.76
0.8646
0.2189
PGSR 4
7.52
13.50
10.51
59.11
73.27
25.73
0.8761
0.2250
QGS (69
8.53
10.02
9.28
55.15
73.66
24.37
0.8624
0.1994
DN-S DN-Splatter
46
6.25
5.29
5.77
61.86
77.13
24.80
0.8549
0,2595
Ours
5.02
77.14
83.35
26.42
0.8874
0.2141
Table 2: Quantitative results surface reconstruction on ScanNet++ and Replica datasets. Our method outperforms other methods most metrics. The best results are marked in bold.
Scan
Replica CD
Acc|
Comp4
CD4
F1t
NCt
Acc|
Compl
F11
NC1
DUS(R [50]
9.70
6.64
38.17
75.27
7.52
7.18
7.35
44.89
68.19
3DGS 18
11.02
10.12
10.57
31.78
77.65
12.40
12.75
12.57
41.64
69.83
2DGS 15
6.89
6.52
6.7
53.46
88.20
9.02
12.05
10.54
52.91
82.28
GOF 63
7.76
6.42
7.09
$7.99
74.75
9.77
8.31
9.04
$4.08
74.34
PGSR
7.32
7.13
53.73
87.30
7.32
9.79
8.56
62.98
83.30
DN Splatttterr [66
4,88
3.44
4.16
75.86
82.06
4.95
6.25
5.60
68.12
80.80
Ours
3.86
3.46
3.66
82.78
90.52
2.80
5.45
4.13
81.90
89.88

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Qualitative Results: Fig. shows mesh comparisons. While 3DGS [88] produces rough surfaces, 2DGS/PGSR's [ flattened Gaussians create local smoothness but suffer from bending artifacts due to the missing of geometric constraints. Despite using depth and normal priors, DN-Splatter [46] still has inaccurate wall positioning some scenes, and the lack of normal consistency causes roughness. Our method uniquely obtains both geometrically accurate planes and smooth surfaces with our planar and geometric priors, serving as high-fidelitg indoor reconstruction method. The quality of reconstruction is also demonstrated by the novel view rendering results shown in Fig.
GT
3DGS
PGSR
DN-Splatter
Ours
Figure 4: Novel view synthesis comparison. With the introduction planar and geometric supervi- sion, PlanarGS can effectively eliminate artifacts present in other methods.
4.2 Ablation Study
To validate the effectiveness of PlanarGS's components and the robustness of the pre-processing stage, we conducted ablation studies on the MuSHRoom and Replica datasets.
Module Effectiveness: We begin with ablation planar prior generation, comparing against the task-specific SOTA model ZeroPlane [29] and the vision-langual foundation model Grounded- SAM 39 without cross-view fusion and geometrie inspection. As Tab. 3 in cases of inaccurate planar priors, co-planarity constraints may act on erroneous regions, leading to a reduction in recon- struction precision. A visual comparison of planar priors can found in Fig. where our planar priors effectively separate different planes and exclude non non-plann non-planar clutter.
We further analyze the effect of each constraint in Gaussian optimization. When without the planarity constraint, as in Fig- depth and normal priors provide geometric constraints only at the local scale, leading surface irregularities in large plane regions. As be seen from the Tab. 3 in the absence of geometric priors from pretrained models, the co- co-planarity constraint contributes more significantly the reconstruction results. However, without geometric prior constraint, scale-aware supervision is missing, causing large planes under the co-planarity constraint to tilt and deviate as whole, as Fig. Finally, when the consistency constraint between rendered normals and surface normals is absent, the flattened Gaussians fail to align with surfaces. This absence affects the normal accuracy of the mesh, displaying as rough surfaces particularly in regions without our co co-planarity constraint, as shown in Tab. Fig. 6
Table Ablation of module effectiveness on the coffee room MuSHRoom dataset. The last row denotes our full model with all components enabled.
Pre-proc.
Opt. Constraints
Metrics CDJ
Planar Prior
Co-planar:
Geom. Prior DN Consist.
Acc./.
Compl.
F1f
NC1
ZeroPlane
4,76
5.73
5.25
62.49
82.41
GroundedSAM
2.95
3.92
3.43
80.22
84.66
LP3 pipeline
2.83
3.53
3.18
85.62
84.63
LP3 pipeline
4.63
5.01
4.82
73.96
80.62
LP3 pipeline
6.93
6.26
6.59
64.11
74.42
I.3 pipeline
2.57
3.17
2.87
88.19
81.95
LP3 pipeline
2.55
3.40
2.98
87.32
85.28
To validate the robustness of our model, we perform extra ablations, as in Tab. 4 The multi-view foundation model can be substituted with VGGT 477. allowing this stage to be completed within few seconds. We also replace the vision-language foundation model with Yorld [10] and SAM (19), and add "blackboard, sofa" in addition to regular prompts
Model Robustness:

---

for the Replica dataset. Both changes have minimal impact on the reconstruction results. These results highlight the robustmess of our pipeline for Language-Prominn Prompted Planar Priors (LP3) and prior supervision, while indicating that improvements in foundation models may further boost the performance of our approach.
Image
ZeroPlnne-plane
ZemoPlans-mesh
Ours-planc
Ours-mesh
Image
Grounks/SAM-plane
GronndedSAM-one
Ours-plame
Ours-mesh
Figure 5: Ablation of planar priors. ZeroPlane tends to wrongly segment cluttered objects into planar regions, while using GroundedSAM without the pipeline for Language Prompped Planar Priors (LP3) cannot distinguish different planes within a single object. Neither of them can provide reliable planar priors for Gaussian optimizing.
w/o co-planar
w/ co-planar
w/o geo-prior
w/ gco-prior
w/o dn-consist
W/ dn-consist
Figure 6: Visualization of constraint effectiveness. Left to right: results without and with co- planarity, geometric prior, and depth normal consistency constraints. Improvements are highlighted in red boxes. The enhancements show proposed constraints lead to high- fideliiiyiity reconstruction.
Table 4: Ablation of model robustness the Replica dataset. The last row shows the settings used for our model in the experiments.
Pre-proccessing
Metrics CDJ
MV-FM VGGT [47
VL-FM
Prompts
Acc|.
Comp.
F11
NC1
GroundedSAM
regular
3.96
6.32
5.14
75.41
88.85
DUS13
Yorl 10 +SAM
regular
2.92
5.56
4.24
80.96
89.04
DUS(3
GroundedS AM
more prompts
5.50
4.19
81.43
89.32
DUSB3
GroundedSAM
regular
2.80
5,45
4.13
81.90
89.88
5 Conclusion
We present a novel method, PlanarGS, that incorporates planar and multi-view depth priors into 3DGS, addressing the inaccurate reconstruction of large and texture-less planes commonly seen in indoor scenes. Extensive experiments on Replica, ScanNet++, and MuSHRoom datasets demonstrate that our method outperforms existing approaches in reconstruction. The aaguaag-promppe planar priors make our method flexible to multiple scenes in various domains like VR and robotics.
Limitations: Our planar priors are only effective for detected plane regions, which work well in indoor environments dominated by large flat surfaces. However, these priors offer no improvement for the reconstruction of curved walls natural outdoor scenes lacking man-made planar structures.
Acknowledgments and Disclosure of Funding
This work was supported by the National Key R&D Program China (2022YFR:900800) and the National Science Foundation of China (62073214).
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A Technical Appendices and Supplementary Material
A.1 Additional Inplementation Details
Datasets: We conduct experiments with multi-view images from three indoor datasets. For the Replica dataset [44. we use eight scenes: officeO-office4 and room0-room2. 100 views sampled from each scene. For ScanNet++ [60], we select sequences 8bScaf3398, b20a261 fddf 66c98f4a9b
and 88c1747085 captured by DSLR. Our experimental data the MuSHRoom 40] consists of five short image sequences (coffee_ room, classroom, honka, kokko, and vr_ vr_room) captured by an iPhone.
Experiment Configuration: We employ an NVIDIA A6000 GPU with 48GB of VRAM for DUS13R [50], which ensures each input group to DUSt3R contains 40 images, producing depth maps with better multi-view consistency. For the training process of all scenes, the depth normal consistency constraint Ldn is introduced from 7000 iterations, the co-planarity constraint Lp from 14000 iterations, while the prior depth and normal constraint Lea, Lyn from 7000 and 20000 iterations respectively. This training strategy ensures Gaussians are in relatively accurate positions before introducing planar priors, thereby reducing artifact generation. For novel view reconstruction, we use every eighth image from the input data as the test set, as in 3DGS.
A.2 Additional Ablation Study
We present additional visualization of the ablation study on the MuSHRoom dataset here. Fig. Fig. 9 sequentially show the ablation results of the co-planarity, geometric priors, and the depth normal consistency constraints.
M//
Figure 7: Ablation study of the co-planarity constraint on the MuSHRoom dataset. Improvements are highlighted in red boxes.
Figure 8: Ablation study of the geometric prior constraint the MuSHRoom dataset. Improve- ments are highlighted in red boxes.
Co-planarity Constraint: When the co- co-planarity constraint is absent, Gaussian optimization relies solely on view-wise depth and normal priors, only providing local geometric guidance of the current view. The lack of global guidance introduces geometric inconsistency into the reconstructed scene, causing large planes fragment into multiple surfaces.
15

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Figure 9: Ablation study of the depth normal consistency constraint the MuSHRoom dataset. Improvements are highlighted in red boxes.
GOF
QGS
FDS
Ours
Ours(colored mesh)
Figure 10: Additional comparison of mesh reconstruction on the MuSHRoom dataset. Compared with these recent methods, our reconstruction results are noticeably smoother in planar regions.
Geometric Prior Constraint: Without depth priors from the multi-view foundation model [50]. reconstruction in texture-less regions under photometric constraint lacks essential 3D structural and scale guidance, causing significant positional inaccuracy of Gaussians. In such cases, imposing co-planarity constraints those artifacts may generate tilted or depth-inaccurate planes.
Depth Normal Consistency: The rotation of flattened Gaussians remains unconstrained without the depth normal consistency constraint, they tend to be perpendicular to reconstructed surfaces. This results in surface roughness similar to that observed in meshes generated by 3DGS (18).
A.3 Additional Results
Surface Reconstruction: We present visual comparison of mesh reconstruction the MuSH- Room dataset between our method and GOF [63], QGS [69] and FDS 7 in Fig. 10 Additionally, we
present more extensive mesh reconstruction comparisons with the 3DGS [8] 2DGS [15], PGSR [4] and DN-Splatter [46] across the three datasets in Fig. F3g. g 15 PlanarGS achieves high-fidelity Gaussian reconstruction all datasets, presenting significantly lower global and local errors and substantially outperforming previous methods.
Novel View Synthesis: We present the novel view synthesis results on the MuSHRoom dataset in Fig. I1 comparing our method with the ground truth, 3DGS (18), PGSR 4. and DN-Splatter [46). be seen that in low-texture and reflective planar regions, other methods lack geometric constraints and produce artifacts in novel views due to the absence of planar priors, whereas our results are closer the ground truth.
Plane Masks and Rendering Results: More comparisons with ZeroPlane (29 and Grounded- SAM (39] in terms of planar-prior generation are shown in Fig. 12 We also show visualizations of planar prior masks and rendering results of PlanarGS on three datasets in Fig. Fig. 18] The first row displays the RGB images rendered by PlanarGS following Eq. 3 the second row shows the plane masks obtained by our pipeline for Language-Prompted Planar Priors (see Sec. 3.2). while
the third and fourth rows exhibit the normal and depth maps rendered by PlanarGS through Eq. 7 and Eq. 9 respectively. Our method achieves robust planar prior masks and accurate RGB and geometric rendering, maintaining geometric consistency without compromising the rendering and reconstruction accuracy of Gaussian splatting.
16

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2
2
2
GT
3DGS
PGSR
DN-Splatter
Ours
Figure 11: Additional novel view synthesis visualization. The highlighted regions are enlarged or marked with red boxes.
Image
ZeroPlane
GroundedSAM
LP3(Ouur)
Figure 12: Comparison of planar priors. ZeroPlane fails to distinguish fine details or curved surfaces as non non-planar regions, while GroundedSAM suffers from plane omission and merges planar regions.
17

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Ours (texture)
3DGS
2DGS
PGSR
DN-Sptrtter
Ours
GT
coffee room
classroom
honka
kokko
Figure 13: Additional qualitative comparison on the MuSHRoom dataset. We color the mesh according surface normal directions facilitate visual assessment of planarity consistency. Our meshes colored with textures are presented in the first row as references.
18

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Ours (texture
3DGS
2DGS
PGSR
DN-Splatter
Ours
GT
office0
office3
room1
room2
Figure 14: Additional qualitative comparison on the Replica dataset. We color the mesh according to surface normal directions facilitate visual assessment of planarity consistency. Our meshes colored with textures are presented in the first row as references.
19

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Ours (textre)
3DGS
2DGS
PGSR
DN-Splatter
Ours
GT
8b5caf3398
b20a261 fdf
Figure 15: Additional qualitative comparison on the ScanNet++ dataset. We color the mesh according surface normal directions facilitate visual assessment of planarity consistency. Our meshes colored with textures are presented in the first row as references.
20

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RGB
Plane Plane-mask -ask
Normal
Depth
coffee room
noffeerroom
classroom
honka
kokko
Vr room
Figure 16: Rendering and plane-mane mask results on the MuSHRoom dataset.
RGB
Plane- mask
Normal
Depth
8b5caf3398
b20a261 fdf
Figure 17: Rendering and plane-mase maak results on the ScanNet++ dataset.
21

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RGB
Plane- mask
Normal
Depth
officel
office2
office4
room0
RGB
Plane- mask
Normal
Depth
office0
office3
room
room2
Figure 18: Rendering and plane-mask results on the Replica dataset.
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NeurIPS Paper Checklist
1. Claims
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Question:
Answer: [Yes]
Justification: We claim to obtain high-fidelity image-based 3D reconstruction on indoor scenes based 3D Gaussian Splatting. Our contributions can be found both in the abstract and at the end of the introduction. Our claims do match our theoretical and experimental results.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as is clear that these goals attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification:
We discuss our limitations of application scenes in the conclusion section.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "I "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect the scope of the claims made, e.g., if the approach was only tested on a few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
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Reviews:
[{"review_id": "Ek0cDsCeCu", "reviewer": "Reviewer_zbUe", "summary": "This paper introduces planar and geometric depth priors into the 3D Gaussian Splatting (3DGS) framework to address challenges in indoor scene reconstruction, particularly in texture-less and large regions. The authors leverage pre-trained vision-language segmentation models for plane detection and utilize DUST3R to extract depth and surface normal information. They propose two supervision terms—a planar-prior loss and a geometric-prior loss—that guide the optimization of 3D Gaussians toward more accurate planar and depth-aware representations. Final mesh reconstructions are obtained using a TSDF fusion approach. Experiments on selected scenes from the Replica, ScanNet++, and MuSHRoom datasets demonstrate improved performance in reconstructing planar structures within indoor scenes.", "questions": "1. None of the comparisons include the original input images, making it difficult to qualitatively assess whether the reconstructions accurately reflect the real scenes. Visual comparison against the source images is essential for evaluating geometric and appearance fidelity.\n2. The paper opts for a general-purpose vision-language segmentation model paired with DUST3R for surface normal estimation. However, this raises a key question: why not use a task-specific method designed for indoor surface normal estimation? Many recent methods can handle both structured and texture-less regions robustly without requiring text prompts. The use of vision-language models adds complexity and fragility without clear advantages in this domain.\n\n3. It is unclear whether the segmentation prompts need to be modified for each new scene. If so, this drastically limits the scalability and practicality of the method. Scene-specific prompt engineering is not viable for real-world applications.", "limitations": "1. The method relies heavily on external models—particularly segmentation foundation models and DUST3R. Any failure or mismatch in these upstream models directly impacts the quality and stability of the final reconstruction.\n2. The proposed pipeline is tailored for indoor environments with large, flat, texture-less surfaces. It is not easily extendable to more diverse indoor environments (e.g., with cluttered small-scale texture-less objects like books or paintings), let alone outdoor scenes, which also include planar regions but typically present very different geometric and appearance characteristics.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 1, "strengths_and_weaknesses": "Strengths:\n1. The paper is clearly written and provides sufficient implementation details to enable reproducibility.\n2. The idea of leveraging pre-trained foundation models to enhance 3D reconstruction is timely and relevant.\n3. The authors introduce novel supervision strategies for both planar and non-planar regions during 3D Gaussian Splatting (3DGS) optimization, which improves the accuracy and robustness of the reconstruction results.\n\nWeaknesses:\n1. Limited Novelty: The paper primarily integrates existing pre-trained models into 3DGS optimization for a specific indoor reconstruction task. This integration feels more like an engineering effort than a contribution of fundamentally new techniques. The technical novelty is marginal.\n2. The method targets indoor scenes with large, texture-less planar surfaces. However, this scope is quite narrow, and the method’s success depends on prior knowledge of such regions, which must be explicitly specified via prompts for segmentation. This reliance hinders generalization.\n3. Heavy Dependence on Pretrained Models: The pipeline depends critically on the success of external models (e.g., segmentation and DUST3R). Failure in any of these steps can cause the entire system to break down, raising concerns about robustness. Compared to traditional methods like COLMAP, this approach may be less reliable.\n4. The experimental benchmark is extremely limited—only 15 test scenes in total. For instance, just 2 scenes were selected from the 460-scene ScanNet++ dataset, both featuring white walls. This narrow and selective evaluation raises concerns about cherry-picking and makes it difficult to assess the general effectiveness of the method. Quantitative results are therefore not convincing at all.\n5. Incomplete Comparisons: If the paper claims to advance 3D reconstruction, it should compare against strong reconstruction baselines such as COLMAP, DUST3R, and 3DGS-based methods like SUGAR. Furthermore, if the focus is on 3DGS, the paper should report rendering metrics and visual results, as 3DGS is specifically designed for high-quality rendering. Their omission is a major oversight.", "was_revised": true, "final_justification": "I appreciate the authors' detailed answers and all the additional experiments. After reading the response and other reviewers' comments, some of my concerns have been addressed. However, several issues remain. This paper works in a very narrow use-case, with all results shown in ideal scenarios with large planar areas, which are uncommon in indoor spaces due to decoration. The authors also admit in their rebuttal that texture reduces the benefits of their design. They haven't adequately addressed my concern about dependency on fundamental models - when these models fail, their method fails, making it less reliable than COLMAP. A handful of results isn't sufficient to draw conclusions about generalization and robustness. Additionally, while I appreciate the engineering efforts, I believe the paper lacks sufficient novelty. Therefore, I'll raise my score slightly, but still consider the paper below the bar for NeurIPS.", "rebuttal": "- **Q1:** Lack of qualitative and quantitative comparison for novel view synthesis\n\n- **A1:** We additionally report **photometric metrics** of both the ScanNet++ and Mushroom datasets.\n\n      Photometric Metrics on Four Scenes of the ScanNet++ Dataset\n\n  |             | PSNR $\\uparrow$ | SSIM $\\uparrow$ | LPIPS $\\downarrow$ |\n  | :---------: | :-------------: | :-------------: | :----------------: |\n  |    3DGS     |      24.93      |     0.8747      |       0.2419       |\n  |    2DGS     |      25.59      |     0.8804      |       0.2394       |\n  |     GOF     |      25.68      |     0.8808      |       0.2363       |\n  |    PGSR     |      25.43      |     0.8799      |       0.2379       |\n  | DN-Splatter |      22.99      |     0.8401      |       0.1984       |\n  |    Ours     |      24.64      |     0.8700      |       0.2536       |\n\n      Photometric Metrics on Four Scenes of the Mushroom Dataset\n\n  |             | PSNR $\\uparrow$ | SSIM $\\uparrow$ | LPIPS $\\downarrow$ |\n  | :---------: | :-------------: | :-------------: | :----------------: |\n  |    3DGS     |      25.79      |     0.8775      |       0.2059       |\n  |    2DGS     |      25.67      |     0.8744      |       0.2265       |\n  |     GOF     |      24.76      |     0.8646      |       0.2189       |\n  |    PGSR     |      25.73      |     0.8761      |       0.2250       |\n  | DN-Splatter |      24.81      |     0.8537      |     **0.1394**     |\n  |    Ours     |    **26.42**    |   **0.8874**    |       0.2141       |\n\n  PlanarGS obtains strong visual quality, **better than our baseline method, PGSR**, while significantly improving surface reconstruction accuracy. **Visualizations of rendered results** are available in the Supplementary Material.\n\n- **Q5:** Limited experimental benchmarks: concerns about potential cherry-picking in ScanNet++（2 scenes with white walls).\n- **A5:** We conduct further experiments on two more ScanNet++ scenes — a storage room (scene 66c98f4a9b) and a bathroom (scene 88cf747085) **with colored tiled walls**. As shown in the results below. Although highly textured scenes may reduce the advantage of our planar prior supervision, PlanarGS continues to exhibit **strong reconstruction performance**. (See Table in **A1** for NVS results.)\nReconstruction Results on 66c98f4a9b of the Scannet++ Dataset\n\n |             | Acc $\\downarrow$ | Comp $\\downarrow$ | Prec $\\uparrow$ | Recall $\\uparrow$ | CD $\\downarrow$ | F-score $\\uparrow$ | Normal C. $\\uparrow$ |\n  | :---------: | :--------------: | :---------------: | :-------------: | :---------------: | :-------------: | :----------------: | :------------------: |\n  |    3DGS     |       8.49       |       12.48       |      52.58      |       56.02       |      10.49      |       54.25        |        65.40         |\n  |    2DGS     |       6.74       |       12.14       |      64.09      |       58.48       |      9.44       |       61.16        |        74.94         |\n  |     GOF     |       7.78       |       9.39        |      55.05      |       63.01       |      8.59       |       58.76        |        68.72         |\n  |    PGSR     |       6.44       |       9.50        |      65.67      |       61.58       |      7.97       |       63.57        |        80.34         |\n  | DN-Splatter |       3.56       |       2.30        |      80.60      |       92.55       |      2.93       |       86.16        |        78.00         |\n  |    Ours     |     **2.88**     |     **2.15**      |    **87.20**    |     **93.00**     |    **2.51**     |     **90.00**      |      **86.64**       |\n\nReconstruction Results on 88cf747085 of the Scannet++ Dataset\n\n|             | Acc $\\downarrow$ | Comp $\\downarrow$ | Prec $\\uparrow$ | Recall $\\uparrow$ | CD $\\downarrow$ | F-score $\\uparrow$ | Normal C. $\\uparrow$ |\n  | :---------: | :--------------: | :---------------: | :-------------: | :---------------: | :-------------: | :----------------: | :------------------: |\n  |    3DGS     |      10.42       |       9.35        |      32.10      |       38.50       |      9.89       |       36.01        |        66.17         |\n  |    2DGS     |       6.31       |       6.78        |      57.48      |       56.29       |      6.54       |       56.88        |        84.80         |\n  |     GOF     |       6.71       |       4.55        |      60.86      |       75.76       |      5.63       |       67.50        |        70.73         |\n  |    PGSR     |       6.56       |       6.25        |      57.76      |       60.44       |       6.4       |       59.07        |        81.58         |\n  | DN-Splatter |    **2.96**     |       1.66        |    **89.49**    |     **98.22**     |    **2.31**     |     **93.65**      |        86.85         |\n  |    Ours     |       3.48       |     **1.64**      |      86.29      |       96.49       |      2.56       |       91.11        |      **90.94**       |\n\nWe have to **clarify** that our dataset selection in the paper follows the **common practice in prior indoor 3DGS methods**, such as DN-Spatter, and the overall number of scenes we use is **comparable to those in related works**.\n- **Q6:** Insufficient comparisons with other baselines.\n\n- **A6:** We report results of **SuGaR** on **TnT** from prior papers, and conduct experiments on another recent Gaussian-based reconstruction method, **GOF**, as in **A7 Table**. We also compare the reconstruction results between our method and the multi-view foundation model we use—**DUSt3R**. Across all three datasets, our **high-fidelity** 3DGS pipeline significantly **outperforms** the pretrained model.\n\n  \n      Reconstruction Comparison with DUSt3R on the MushRoom dataset\n\n\n  |          | Acc $\\downarrow$ | Comp $\\downarrow$ | Prec $\\uparrow$ | Recall $\\uparrow$ | CD $\\downarrow$ | F-score $\\uparrow$ | Normal C. $\\uparrow$ |\n  | :------: | :--------------: | :---------------: | :-------------: | :---------------: | :-------------: | :----------------: | :------------------: |\n  |  DUSt3R  |       8.01       |       6.22        |      54.25      |       61.03       |      7.11       |       57.31        |        72.96         |\n  | PlanarGS |     **3.95**     |     **5.02**      |    **79.89**    |     **74.65**     |    **4.49**     |     **77.14**      |      **83.35**       |\n\n\n      Reconstruction Comparison with DUSt3R on the Replica dataset\n  \n\n  |          | Acc $\\downarrow$ | Comp $\\downarrow$ | Prec $\\uparrow$ | Recall $\\uparrow$ | CD $\\downarrow$ | F-score $\\uparrow$ | Normal C. $\\uparrow$ |\n  | :------: | :--------------: | :---------------: | :-------------: | :---------------: | :-------------: | :----------------: | :------------------: |\n  |  DUSt3R  |       7.52       |       7.18        |      41.06      |       49.84       |      7.35       |       44.89        |        68.19         |\n  | PlanarGS |     **2.80**     |     **5.45**      |    **89.28**    |     **75.67**     |    **4.13**     |     **81.90**      |      **89.88**       |\n\n  \n      Reconstruction Comparison with DUSt3R on the Scannet++ dataset\n  \n\n  |          | Acc $\\downarrow$ | Comp $\\downarrow$ | Prec $\\uparrow$ | Recall $\\uparrow$ | CD $\\downarrow$ | F-score $\\uparrow$ | Normal C. $\\uparrow$ |\n  | :------: | :--------------: | :---------------: | :-------------: | :---------------: | :-------------: | :----------------: | :------------------: |\n  |  DUSt3R  |       9.70       |       6.64        |      31.08      |       50.12       |      8.17       |       38.17        |        75.27         |\n  | PlanarGS |     **3.86**     |     **3.46**      |    **81.56**    |     **84.05**     |    **3.66**     |     **82.78**      |      **90.52**       |\n\n  Actually, as in the Experiments part (line 268-271), our baselines have included the **state-of-the-art Gaussian surface reconstruction methods**, specifically **2DGS**, **PGSR**, as well as the original 3DGS and DN-Splatter that used to be **SOTA on indoor scenes** and incorporates **pre-trained models as well**. We did not include **SuGaR** in our main experiments, as it is an earlier Gaussian-based method whose performance **has been surpassed** by subsequent approaches  — including **2DGS** and **PGSR**, which we have already evaluated. \n\n- **Q7:** The proposed pipeline is not easily extendable to more diverse indoor environments, let alone outdoor scenes.\n\n- **A7:** We have additionally conducted experiments on the Meetingroom and Barn scenes from the **TnT** dataset. Meetingroom features a **large-scale, semi-open indoor space with a complex roof structure**, while Barn represents an **outdoor man-made scene**. In both cases, our method achieves **high-quality** reconstructions. ( \"-\" indicates that the method **failed** to generate a mesh.)\n  |             | 3DGS  | SuGaR | 2DGS  | GOF  | PGSR | DN-Splatter |   Ours   |\n  | :---------: | :---: | :---: | :---: | :--: | :--: | :---------: | :------: |\n  |    Barn     | 0.13  | 0.14  | 0.36  | 0.51 | 0.66 |      -      | **0.70** |\n  | Meetingroom | 0.01  | 0.15  | 0.16  | 0.28 | 0.29 |      -      | **0.55** |\n  |    time     | 14.3m |  2h   | 34.2m |  2h  | 1.2h |    1.1h     |    1h    |"}, {"review_id": "4c13YkjT8I", "reviewer": "Reviewer_q67a", "summary": "The paper proposes the PlanarGS, which leverages vision-language plane priors and geometric priors to guide 3D Gaussian splatting, addressing the issue of geometric ambiguity in 3D reconstruction of low-texture planar regions in indoor environments. Experimental results demonstrate that its performance outperforms existing methods.", "questions": "🤔\n\n1. The experiments claim to effectively address indoor environments with numerous low-texture planes, yet the presented cases are overly simplistic. I am curious: would the performance of the proposed method be affected in environments with a large number of curved furniture pieces?\n2. The work uses Grounded SAM for segmentation and relies on DUSt3R for geometric verification and refinement. However, in scenarios with insufficient information (e.g., specular reflections, backlit environments), could this approach introduce additional errors, leading to harm to model robustness?\n3. The paper only compares 4 methods. To my knowledge, other methods such as Manhattan achieve comparably competitive visualization results. The absence of comparisons with such works would significantly undermine the persuasiveness of the proposed method.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths😊**\n1. The paper primarily introduces planar constraints and geometric priors to enhance geometric accuracy in low-texture regions, resolving the performance limitations of existing methods (e.g., 2D/3DGS, PGSR) when handling large-scale textureless planes.\n2.  Experiments cover multiple types of indoor datasets, with both quantitative and qualitative results significantly outperforming baseline methods such as 3DGS and PGSR, validating the effectiveness of the framework.\n\n**Weaknesses😭**\n1. While ablation studies verify the effectiveness of individual components, they fail to compare the impact of different vision-language models on plane detection, leading to insufficient verification of robustness.\n2. Multi-view geometric priors (e.g., DUSt3R) incur high computational costs, which may hinder real-time performance. No mention is made of efficiency optimizations for practical deployment.\n3. Plane priors rely on detection results from pre-trained models. How is the introduction of additional errors in this process prevented?", "was_revised": true, "final_justification": "The author seems to have addressed my concerns. However, since this submission was randomly assigned to me and it is not my area of expertise, despite my best efforts in reviewing, I still suggest that the AC take into account the opinions of other reviewers more. Thank you.", "rebuttal": "- **Q1:** Lack of comparison of the impact of different vision-language models on plane detection, leading to insufficient verification of robustness.\n- **A1:** We supplement **our plane segmentation method with quantitative comparisons against other approaches**. \n\n|             | precision $\\uparrow$ | recall $\\uparrow$ | f-score $\\uparrow$ |\n|:-----------:|-------------------|----------------|-----------------|\n| PlaneRecNet |             0.534 |          0.364 |           0.416 |\n| Ours        |             **0.582** |          **0.527** |           **0.538** |\n\nAs stated in our abstract, our method primarily focuses on improving the geometric reconstruction of 3D Gaussians in low-texture plane regions of indoor scenes. Therefore, we emphasize the surface reconstruction quality and related contributions. Here, we supplement **our plane segmentation method with quantitative comparisons against other approaches**. Unfortunately, due to the rebuttal limitations, we are unable to present visual results. We will consider conducting the robustness experiment you mentioned, replacing GroundedSAM with other vision-language foundation models.\n- **Q2:** Multi-view geometric priors (e.g., DUSt3R) incur high computational costs, which may hinder real-time performance. \n- **A2:** Our work primarily addresses the issue of supervising 3DGS using pre-trained multi-view geometric models, whereas improving the foundation model itself is **beyond the scope of this paper**, as stated in the third contribution of our Introduction. Our method does not rely on a specific foundation model and can be robustly adapted to alternatives. The latest **VGGT** model enables reconstruction of **hundreds of views within seconds**. Here, we provide results on the Replica dataset after replacing DUSt3R with VGGT. \n    |                 | Acc $\\downarrow$ | Comp $\\downarrow$ | Prec $\\uparrow$ | Recall $\\uparrow$ | CD $\\downarrow$ | F-score $\\uparrow$ | Normal C. $\\uparrow$ |\n  | :-------------: | :--------------: | :---------------: | :-------------: | :---------------: | :-------------: | :----------------: | :------------------: |\n  | DUSt3R-PlanarGS |       2.80       |       5.45        |      89.28      |       75.67       |      4.13       |       81.90        |        89.88         |\n  |  VGGT-PlanarGS  |       3.96       |       6.32        |      81.87      |       69.91       |      5.14       |       75.41        |        88.85         |\n\nReplacing the foundation model within the PlanarGS pipeline is feasible and results in only minor changes to the evaluation metrics. Future foundation models are expected to further enhance our reconstruction efficiency and performance.\n- **Q3:** Plane priors rely on detection results from pre-trained models. How is the introduction of additional errors in this process prevented?\n- **A3:** Our method **does not rely on highly accurate outputs** from the pre-trained models. Our strict **cross-view fusion** and **geometric inspection** modules significantly enhance the recall and precision of our open-vocabulary plane segmentation method, as described in Section 3.2 of the paper. Moreover, 3D reconstruction **inherently benefits from multi-view consistency**, and the **geometric priors** from the pre-trained model also provide strong supervision. As a result, occasional segmentation errors in individual views **barely** affect the overall reconstruction quality.\n- **Q4:** The presented cases are overly simplistic. Would the performance of the proposed method be affected in environments with a large number of curved furniture pieces?\n\n- **A4:** We have to clarify that the presented cases **are not** overly simplistic, and our method demonstrates **strong generalization ability** in cluttered scenes. Our dataset selection follows the **common practice in prior indoor 3DGS methods**, such as DN-Splatter. In particular, the Mushroom dataset covers a wide range of real-world complex indoor structures, as stated in Implementation Details. As you can see in Supplementary Material **Fig8,11**, **the scenes are cluttered with various objects and curved furniture**. By providing GroundedSAM with prompts related to planar objects, curved clutter and furniture are typically **not identified as plane regions**. Occasional segmentation or recognition errors can be effectively filtered out during **the Geometric Inspection** step by leveraging prior normal and depth information, as described in lines 181–186 of Section 3.2. These typically textured objects out of plane masks are then reconstructed with high fidelity through the **existing high-quality Gaussian surface reconstruction baseline**.\n\n- **Q5:** The work uses Grounded SAM for segmentation and relies on DUSt3R for geometric verification and refinement. However, in scenarios with insufficient information (e.g., specular reflections, backlit environments), could this approach introduce additional errors, leading to harm to model robustness?\n\n- **A5:** Our method is **particularly robust** in environments with insufficient information. The foundation models are trained on large-scale data and **demonstrate strong robustness** in regions with insufficient information, which is the key reason we incorporate them into our 3DGS pipeline. Unfortunately, we are unable to show the depth prior images from DUSt3R here. However, as shown in **Figure 3** of the main paper, our method performs well in challenging conditions: the first row features a completely dark scene, the third row contains strong wall reflections and overexposure, and the last row includes specular reflections from a screen. None of these factors compromises our reconstruction quality. Additionally, **Figures 11–13** in the supplementary material further demonstrate that our plane segmentation remains robust under varying lighting conditions. You can have a better look at our mesh results in our **video**.\n\n- **Q6:** The absence of comparisons with works such as Manhattan.\n\n- **A6:** I think you are referring to the **Manhattan-SDF** work here, and we don't think we have to compare with it. As we mentioned in the second paragraph of the Related Work Section, Manhattan-SDF is based on the time-consuming **NeRF** framework, and its MLP fusion approach cannot be directly transferred to Gaussian-based methods. Our method is an indoor scene improvement of the **3DGS** surface reconstruction approach, so we only compare it with Gaussian-based methods that have **comparable training times**."}, {"review_id": "CnpJgKUsHP", "reviewer": "Reviewer_i6Sf", "summary": "This paper proposes PlanarGS, a method that incorporates plane priors detected by vision-language models with multi-view depth-normal geometric priors to enhance indoor 3D Gaussian Splatting. The approach improves the accuracy and completeness of plane detection through cross-view plane fusion and geometric verification. It introduces supervision strategies specifically for planar and non-planar regions, including Gaussian flattening, coplanarity constraints, and depth-normal consistency. Experiments conducted on multiple real and synthetic indoor datasets demonstrate that the method significantly outperforms existing SOTA methods.", "questions": "If the multi-view images of a scene are captured from discrete viewpoints rather than a continuous sequence, how should the neighboring views be selected in the cross-view fusion process?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n1. The proposed method presents a well-designed and technically sound framework for integrating plane priors into 3D scene reconstruction, contributing substantially on a technical level.\n\n2. Qualitative results show high-quality reconstruction of indoor scenes, with very smooth planar regions; the quantitative results also demonstrate significant improvements over previous methods, which is highly convincing.\n\n3. The paper includes comprehensive ablation studies to validate the effectiveness of each design component.\n\n## Weaknesses\n\n1. The method involves a relatively complex pipeline with many steps and heuristic components—for example, using Grounded-SAM for initial plane segmentation, DUST3R for depth and normal extraction, and Canny edge detection for computing the mask of low-texture regions. Some steps also involve hyperparameter choices, such as the confidence threshold in GroundingDINO. Therefore, the robustness of the method remains to be discussed.\n\n2. Minor issues: \"SFM\" is better to be written as \"SfM\"; \"visual\" in line 299 should be corrected to \"vision\" for consistency.", "was_revised": true, "final_justification": "The authors’ rebuttal has addressed my concerns. After carefully reading and considering the other reviews and the rebuttal, I do not see major issues and will maintain my original score (accept). However, I suggest the authors include experimental results on more scenes in the paper.", "rebuttal": "We appreciate your positive feedback on our method and experiments. We address your comments in our below responses.\n\n- **Q1:** The method involves a relatively complex pipeline with many steps and heuristic components, together with hyperparameter choices. Therefore, the robustness of the method remains to be discussed.\n- **A1:** PlanarGS **is** a robust method. The threshold of confidence and canny mask, as well as all the other various hyperparameters in the 3D reconstruction pipeline, are **kept consistent** across all scenes and datasets.\n- **Q2:** Minor issues: \"SFM\" is better to be written as \"SfM\"; \"visual\" in line 299 should be corrected to \"vision\" for consistency.\n- **A2:** Thank you for your careful reading and correction. We will revise them in future versions of the paper.\n- **Q3:** If the multi-view images of a scene are captured from discrete viewpoints rather than a continuous sequence, how should the neighboring views be selected in the cross-view fusion process?\n- **A3:** For unordered input, we reorder them as a sequence before our plane segmentation, by calculating the field-of-view (FOV) similarity between each frames and solve the graph traversal problem."}, {"review_id": "ijQ5BrDhyo", "reviewer": "Reviewer_ew1g", "summary": "The paper introduces PlanarGS, an new 3D Gaussian Splatting (3DGS) framework specifically designed to enhance 3D reconstruction in indoor environments, which often feature large, low-texture areas problematic for standard 3DGS. PlanarGS addresses this by incorporating semantic planar and geometric priors. It first employs a vision-language model to identify planar regions, refining these detections through cross-view fusion. Subsequently, the 3D Gaussians are optimized with two novel supervision terms: one to enforce planar consistency based on these detected regions, and another to guide the Gaussians using depth and surface-normal cues extracted from a separate 3D network. This holistic approach improves geometric accuracy and detail, leading to superior 3D surface reconstruction compared to existing methods.", "questions": "1. What are the main reason for being better than DNSplatter if not the coplanarity? \n2. How does the proposed plane segmentation method compare with other ones? \n3. What is the performance of GOF on these datasets? \n4. What is the photometric accuracy on the evaluated datasets? \n\nI am open to improving my rating, however, all these points are important to me to do so.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "--- Strengths ---\n\nThe proposed method seems to improve geometric accuracy compared to the recent DN-Splatter.  \nHowever, the reason for this is unclear and there are questionable parts in the paper that should be clarified. \n\n--- Weaknesses ---\n\n- Table 4 suggests that the co-planarity constraints offer only minimal, albeit consistent, improvement. This leads to a question about the primary reasons for the reported performance gains, especially since DN-Splatter employs similar depth and normal losses. This must be explained. \n- The paper proposes a multi-plane segmentation pipeline that's never evaluated in isolation or compared against baselines. Given the many existing multi-plane estimation methods (e.g., PlaneSegNet, PlaneRCNN, PlaneSeg, SPL-PlaneTR, PlaneSAM), the authors need to justify why their specific method was chosen for supervising 3DGS, particularly concerning computational cost (needs running VLLMs and Grounded DINO on many images in contrast to, e.g., running a simple plane region growing on a depth map). I do not say that the proposed method may not be more accurate than baselines. However, this must be verified to make the plane segmentation pipeline a contribution. \n- Missing baselines from the tables: [a,b]. GOF [b] is the current state-of-the-art in surface reconstruction from 3DGS, so this must be added to the comparisons. [a] seems to be solving a similar problem as the proposed method. \n- The tables only report the geometric accuracy and photometric ones. It would be important to verify that the photometric accuracy does not decrease. \n- The paper is using different datasets that most of the 3DGS geometric reconstruction papers do that makes it hard to compare with results in other papers. It would help if the authors provide results on Tanks and Temples or DTU too.", "was_revised": true, "final_justification": "The authors addressed my concerns in their rebuttal.", "rebuttal": "- **Q1:** What are the main reasons for being better than DNSplatter if not the coplanarity?\n\n- **A1:**  We would like to **clarify** that the performance gain brought by our co-planarity constraint is **not minimal** and **could not directly compare** to any specific component in DN-Splatter. First, as shown in the ablation study (**Table 4** in the main paper), removing the coplanar constraint leads to a **7%** increase in Chamfer Distance. Additionally, **Figure 5** in the Supplementary Material visually demonstrates the improvement in holistic planar smoothness brought by this constraint. \n\n  We have **included additional ablation studies**, which show that even **without geometric priors**, the coplanar constraint **significantly improves** reconstruction quality. This demonstrates the effectiveness of the planar prior.\n\n  |               | Acc $\\downarrow$ | Comp $\\downarrow$ | Prec $\\uparrow$ | Recall $\\uparrow$ | CD $\\downarrow$ | F-score $\\uparrow$ | Normal C. $\\uparrow$ |\n  | :-----------: | :--------------: | :---------------: | :-------------: | :---------------: | :-------------: | :----------------: | :------------------: |\n  | w/o co-planr  |       2.83       |       3.53        |      88.92      |       82.55       |      3.18       |       85.62        |        84.63         |\n  | w/o geo-prior |       4.63       |       5.01        |      75.7       |       72.3        |      4.82       |       73.96        |        80.62         |\n  |   w/o both    |       6.93       |       6.26        |      64.11      |       65.22       |      6.59       |       64.11        |        74.42         |\n  |  full model   |       2.55       |        3.4        |      91.59      |       83.43       |      2.98       |       87.32        |        85.28         |\n\n  Additionally, our method is built upon the **PGSR codebase**, while DN-Splatter follows a substantially different pipeline. As stated in lines 278–280 of the main paper, DN-Splatter lacks normal consistency constraints. Moreover, its normal priors are derived from a monocular normal estimation model, rather than a multi-view foundation method. Its mesh extraction process also differs from ours. These differences **collectively** lead to DN-Splatter performing worse than even our method without the co-planarity constraint.\n- **Q2**: How does the proposed plane segmentation method compare with other ones?\n\n- **A2:** We have added **quantitative results** for multi-view planar segmentation on the EVOPS Benchmark, for the living-room and office scenes from the ICL dataset. The table includes comparison with the baseline method **PlaneRecNet**.\n\n|             | precision $\\uparrow$ | recall $\\uparrow$ | f-score $\\uparrow$ |\n|:-----------:|-------------------|----------------|-----------------|\n| PlaneRecNet |             0.534 |          0.364 |           0.416 |\n| Ours        |             **0.582** |          **0.527** |           **0.538** |\n\nMany of the multi-plane estimation methods you mentioned, such as PlaneSeg and PlaneSAM, are **not open-sourced** or without checkpoints, making it infeasible to directly compare with them. As shown in the table, our zero-shot open-vocabulary plane segmentation model **outperforms** the strong baseline model **trained explicitly for plane segmentation**. We also obtained excellent qualitative results, which we regretfully cannot include here due to rebuttal constraints.\nWe would like to re-explain our reasons for using the foundation segmentation model, as stated in lines 42–48 of the Introduction. The large-scale training and open-vocabulary input of advanced foundation models make them more **flexible** than task-specific models, resulting in better segmentation **quality** and stronger **cross-scene** generalization. Moreover, the depth priors utilized in our pipeline are **low-resolution results** from DUSt3R. To avoid compromising the accuracy of pixel-level segmentation, we do not use them directly for segmentation, but rather as a source of cross-view geometric information.\n- **Q3:** What is the performance of GOF on these datasets?\n\n- **A3:** We provide reconstruction results including **GOF** on the three datasets, as shown below. \n\nreplica\n\n| Method       | L1↓   | Rel↓  | PSNR↑ | SSIM↑ | RMSE↓ | LPIPS↓ | Acc↑   |\n|--------------|-------|-------|-------|-------|-------|--------|--------|\n| 3DGS         | 12.40 | 12.75 | 43.17 | 40.24 | 12.57 | 41.64  | 69.83  |\n| 2DGS         | 9.02  | 12.05 | 58.30 | 48.45 | 10.54 | 52.91  | 82.28  |\n| PGSR         | 7.32  | 9.79  | 68.64 | 58.22 | 8.56  | 62.98  | 83.30  |\n| DN-Splatter  | 4.95  | 6.25  | 68.76 | 67.56 | 5.60  | 68.12  | 80.80  |\n| **Ours**     | **2.80** | **5.45** | **89.28** | **75.67** | **4.13** | **81.90** | **89.88** |\n| GOF          | 9.77  | 8.31  | 51.26 | 57.62 | 9.04  | 54.08  | 74.34  |\n| Dust3r_mesh  | 7.52  | 7.18  | 41.06 | 49.84 | 7.35  | 44.89  | 68.19  |\n\nscannet++\n\n| Method       | L1↓   | Rel↓  | PSNR↑ | SSIM↑ | RMSE↓ | LPIPS↓ | Acc↑   |\n|--------------|-------|-------|-------|-------|-------|--------|--------|\n| 3DGS         | 9.98  | 10.65 | 38.27 | 42.71 | 10.32 | 40.68  | 69.74  |\n| 2DGS         | 6.65  | 8.48  | 57.90 | 56.55 | 7.56  | 57.17  | 82.65  |\n| PGSR         | 6.77  | 7.63  | 58.42 | 59.29 | 7.20  | 58.79  | 83.07  |\n| DN-Splatter  | 3.80  | 2.46  | 79.84 | 91.46 | 3.13  | 85.22  | 82.30  |\n| **Ours**     | **3.41** | **2.42** | **85.02** | **91.18** | **2.91** | **87.96** | **89.37** |\n| GOF          | 7.76  | 6.42  | 52.70 | 64.52 | 7.09  | 57.99  | 74.75  |\n| Dust3r_mesh  | 7.96  | 5.24  | 41.94 | 64.46 | 6.60  | 50.69  | 75.51  |\n\nmushroom\n\n| Method       | L1↓   | Rel↓  | PSNR↑ | SSIM↑ | RMSE↓ | LPIPS↓ | Acc↑   |\n|--------------|-------|-------|-------|-------|-------|--------|--------|\n| 3DGS         | 12.01 | 11.85 | 35.84 | 41.76 | 11.92 | 38.53  | 62.00  |\n| 2DGS         | 9.16  | 10.27 | 52.31 | 50.84 | 9.71  | 51.50  | 73.65  |\n| PGSR         | 7.52  | 13.50 | 62.68 | 56.33 | 10.51 | 59.11  | 73.27  |\n| DN-Splatter  | 6.25  | 5.29  | 57.69 | 66.72 | 5.77  | 61.86  | 77.13  |\n| **Ours**     | **3.95** | **5.02** | **79.89** | **74.65** | **4.49** | **77.14** | **83.35** |\n| GOF          | 15.65 | 8.50  | 34.09 | 56.97 | 12.07 | 42.57  | 61.47  |\n| Dust3r_mesh  | 8.01  | 6.22  | 54.25 | 61.03 | 7.11  | 57.31  | 72.96  |\n\n  It can be clearly seen that our method consistently **outperforms** GOF by a large margin. In fact, GOF was proposed before PGSR and has already **been surpassed by PGSR** on multiple datasets. Moreover, the mesh extraction process of GOF requires **significant GPU resources**, which is another reason we did not include it in the main comparisons of our paper.\n\n- **Q4:** What is the photometric accuracy on the evaluated datasets?\n\n- **A4:** We report photometric accuracy on the Mushroom and ScanNet++ datasets, and observe comparable performance with the baseline methods. \n\nPhotometric Metrics on Four Scenes of the ScanNet++ Dataset\n\n|             | PSNR $\\uparrow$ | SSIM $\\uparrow$ | LPIPS $\\downarrow$ |\n  | :---------: | :-------------: | :-------------: | :----------------: |\n  |    3DGS     |      24.93      |     0.8747      |       0.2419       |\n  |    2DGS     |      25.59      |     0.8804      |       0.2394       |\n  |     GOF     |      25.68      |     0.8808      |       0.2363       |\n  |    PGSR     |      25.43      |     0.8799      |       0.2379       |\n  | DN-Splatter |      22.99      |     0.8401      |       0.1984       |\n  |    Ours     |      24.64      |     0.8700      |       0.2536       |\n\nPhotometric Metrics on Four Scenes of the Mushroom Dataset\n\n|             | PSNR $\\uparrow$ | SSIM $\\uparrow$ | LPIPS $\\downarrow$ |\n  | :---------: | :-------------: | :-------------: | :----------------: |\n  |    3DGS     |      25.79      |     0.8775      |       0.2059       |\n  |    2DGS     |      25.67      |     0.8744      |       0.2265       |\n  |     GOF     |      24.76      |     0.8646      |       0.2189       |\n  |    PGSR     |      25.73      |     0.8761      |       0.2250       |\n  | DN-Splatter |      24.81      |     0.8537      |     **0.1394**     |\n  |    Ours     |    **26.42**    |   **0.8874**    |       0.2141       |\n\n  The challenging test set of Scannet++ consists of images with viewpoints that significantly deviate from the training sequences. For Mushroom, we follow the protocol used in 3DGS and sample one out of every eight images from the sequence to construct the test set. \n\n- **Q5:** It would help if the authors provide results on Tanks and Temples or DTU too.\n\n- **A5:** We have conducted experiments on the Meetingroom and Barn scenes from the **TnT** dataset as below. In both scenes, our method achieves **high-quality** reconstructions. ( \"-\" indicates that the method **failed** to generate a mesh.)\n\n  |             | 3DGS  | SuGaR | 2DGS  | GOF  | PGSR | DN-Splatter |   Ours   |\n  | :---------: | :---: | :---: | :---: | :--: | :--: | :---------: | :------: |\n  |    Barn     | 0.13  | 0.14  | 0.36  | 0.51 | 0.66 |      -      | **0.70** |\n  | Meetingroom | 0.01  | 0.15  | 0.16  | 0.28 | 0.29 |      -      | **0.55** |\n  |    time     | 14.3m |  2h   | 34.2m |  2h  | 1.2h |    1.1h     |    1h    |\n\n  We did not conduct experiments on the **object-level DTU dataset**, following the practice of other indoor reconstruction works. Although the TnT dataset is scene-level, it contains **only one indoor scene** with ground truth—the Meetingroom. Additionally, we evaluated our method on the **outdoor man-made structure** Barn to demonstrate the potential of our approach to generalize to outdoor reconstruction with plane regions."}]

Author rebuttal:

Meta-review: This paper introduces PlanarGS, which integrates planar priors derived from segmentation foundation models and VLMs with depth and normal priors obtained from multi-view foundation models into the 3DGS pipeline. The goal is to address the difficulty of reconstructing large, low-texture planar surfaces in indoor scenes, where photometric consistency alone often fails to estimate accurate geometry. By introducing coplanarity constraints and depth-normal consistency terms, the proposed method guides the optimization of Gaussians toward more faithful surfaces and achieves improvements in geometric reconstruction.

The reviewers raised concerns that the novelty is somewhat limited, since the proposed approach relies on integrating existing models such as segmentation models, VLMs, and multi-view reconstruction methods, especially regarding the proposed plane segmentation pipeline. However, in the rebuttal, the authors reported that their segmentation pipeline, which combines a VLM with a foundation model, outperforms existing plane segmentation methods. The rebuttal also provided additional comparisons, ablation studies, and evaluations on more diverse datasets, which highlighted the superior performance and robustness of the proposed approach.

Overall, the proposed pipeline is well-motivated, experimentally validated, and addresses an important challenge in indoor 3D reconstruction. The improvements over baselines are consistent and convincing, and the contribution is of clear interest to the community. I recommend acceptance.
Decision: Accept (poster)