Title: AtlasGS: Atlanta-world Guided Surface Reconstruction with Implicit Structured Gaussians
Venue: NeurIPS 2025
Accepted: yes
Keywords: indoor surface reconstruction

gaussian splatting
Abstract: 3D reconstruction of indoor and urban environments is a prominent research topic with various downstream applications. 
However, existing geometric priors for addressing low-texture regions in indoor and urban settings often lack global consistency.
Moreover, Gaussian Splatting and implicit SDF fields often suffer from discontinuities or exhibit computational inefficiencies, resulting in a loss of detail. To address these issues, we propose an Atlanta-world guided implicit-structured Gaussian Splatting that achieves smooth indoor and urban scene reconstruction while preserving high-frequency details and rendering efficiency. By leveraging the Atlanta-world model, we ensure the accurate surface reconstruction for low-texture regions, while the proposed novel implicit-structured GS representations provide smoothness without sacrificing efficiency and high-frequency details. Specifically, we propose a semantic GS representation to predict the probability of all semantic regions and deploy a structure plane regularization with learnable plane indicators for global accurate surface reconstruction. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in both indoor and urban scenes, delivering superior surface reconstruction quality.

AtlasGS: Atlanta-world Guided Surface Reconstruction with Implicit Structured Gaussians

Abstract

3D reconstruction of indoor and urban environments is a prominent research topic with various downstream applications. However, existing geometric priors for addressing low-texture regions in indoor and urban settings often lack global consistency. Moreover, Gaussian Splatting and implicit SDF fields often suffer from discontinuities or exhibit computational inefficiencies, resulting in a loss of detail. To address these issues, we propose an Atlanta-world guided implicit-structured Gaussian Splatting that achieves smooth indoor and urban scene reconstruction while preserving high-frequency details and rendering efficiency. By leveraging the Atlanta-world model, we ensure the accurate surface reconstruction for low-texture regions, while the proposed novel implicit-structured GS representations provide smoothness without sacrificing efficiency and high-frequency details. Specifically, we propose a semantic GS representation to predict the probability of all semantic regions and deploy a structure plane regularization with learnable plane indicators for global accurate surface reconstruction. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in both indoor and urban scenes, delivering superior surface reconstruction quality.

1 Introduction

Recently, indoor and urban 3D reconstruction from multi-view images has emerged as a popular research area, fueled by its applications in digital twins [1-3], robotic navigation [4-6], and augmented reality [7]. These technologies demand accurate and efficient reconstruction of real-world environments. However, man-made scenes often contain large low-texture planar regions, such as floors, ceilings, and unadorned walls, which pose significant challenges for image-based 3D reconstruction. Traditional multi-view stereo methods struggle on these textureless surfaces due to the lack of distinctive visual features, leading to incomplete or distorted geometry. Geometric priors play a crucial role in addressing this challenge. Monocular geometric priors provide locally smooth geometry signals for low-texture regions [8], but they often lack global consistency across different viewpoints, often resulting in inconsistent geometry like bumpy surfaces. Alternatively, the Manhattan-world assumption [9] leverages planar priors to address low-texture regions in man-made scenes but cannot be applied in urban scenes where buildings are not mutually orthogonal structures, such as the building marked by the yellow rectangle in Fig. 1.

Concurrently, 3D representation methods for indoor and urban reconstruction have evolved rapidly and achieved remarkable success. For example, 2DGS [10] employs Gaussian splitting (GS) with surfel primitives to efficiently and effectively reconstruct complex geometry. However, its discrete primitives induce discontinuities in surface reconstruction, resulting in broken surfaces in low-texture or under-observed regions, as depicted in Fig. 1. Previous implicit SDF representations [11-14] leverage the inductive continuity of coordinate-based multi-layer perceptrons (MLPs) to recover complete surfaces in these challenging regions. However, they are computationally expensive and struggle to represent complex geometries. Some methods [15-17] have explored the simultaneous learning of both representations, using implicit methods to guide GS optimization for smoother outcomes. Unfortunately, this mutual interaction often compromises reconstruction quality.

Based on these observations, we identify two critical challenges: 1) A globally consistent geometric prior is essential to regularize low-texture regions in both indoor and urban reconstructions; 2) A 3D representation is needed that retains the efficiency and detail-preserving capabilities of GS while incorporating the smoothness of implicit methods.

In this paper, we propose an Atlanta-world guided implicit-structured Gaussian Splatting for indoor and urban scene reconstruction.

First, the man-made indoor and urban environments can be described as an Atlanta world model [18] where there is one vertical direction aligned with gravity and multiple horizontal directions oriented from walls or urban buildings. To establish globally consistent and accurate geometry in low-texture regions, such as floor, ceiling, and walls, we incorporate this global geometric assumption into GS optimization, thereby regularizing the geometric relationships among these regions. Specifically, we propose a semantic GS representation to predict the semantic probability of floor, ceiling, and wall in the 3D scene. Besides, we design a structure plane regularization with learnable explicit plane indicators for globally accurate surface reconstruction.

Secondly, we propose a novel implicit-structured Gaussian representation, which integrates the continuity of implicit functions with the efficiency and detail preservation of 2DGS. Unlike prior works that simply overlay implicit priors on Gaussian optimization, we embed implicit voxel grids within the Gaussian Splatting framework, allowing implicit geometry to act as a smooth regularizer while maintaining high-frequency representation. Besides, we adopt a view-independent decoding on GS spatial distribution to enhance the geometric consistency across multiple viewpoints. Our representation not only improves the smoothness of surface reconstruction but also facilitates the efficient modeling of intricate geometry, striking a better balance between accuracy and efficiency.

The contribution of our paper can be summarized as: 1) We propose a novel Atlanta-world guided implicit-structured Gaussian Splatting to achieve smooth indoor and urban scene reconstruction while preserving high-frequency details and rendering efficiency. 2) To integrate the Atlanta-world assumption, we design a semantic GS representation to predict the semantic probability of low-texture regions such as floor, ceiling, and walls, and propose a structure plane regularization with learnable explicit plane indicators to regularize the global geometry of these low-texture regions. 3) Extensive experiments in indoor and urban scenes demonstrate the effectiveness of our method. We show the best surface reconstruction quality quantitatively and qualitatively compared to other state-of-the-art methods.

2 Related Works

Neural Implicit Surface Reconstruction. Neural Radiance Fields [19] (NeRF) designs a neural implicit representation to reconstruct the scene from multi-view 2D images. To obtain the scene surface, some NeRF variants [11, 12, 20] combine SDF-based neural representation with volume rendering for better geometry reconstruction. In addition, to obtain better reconstruction results in some challenging scenarios, some research works introduced more prior information during the optimization process, such as geometric regularization [13], monocular depth [21-23], normal [14, 23, 24, 20], and semantics [13, 25, 26]. However, due to the limited representation capacity of MLPs [27], relying solely on MLPs may result in slow optimization and poor reconstruction performance of large scenes. Therefore, additional feature encoding is used to enhance the scene representation ability and speed up the reconstruction, such as dense feature grid [28, 22], hash table [29, 30], sparse voxel [31-33], and tetrahedron [34-38]. However, despite the use of feature encoding, all of these implicit methods still require hours of training and exhibit low inference times and insufficient detail during the reconstruction. In contrast, our method enables efficient surface reconstruction (< 30 minutes) with high-quality details.

Surface Reconstruction with Gaussian Splatting. 3D Gaussian Splatting [39] (3DGS) has emerged as a promising technique for efficient and high-quality novel view synthesis. Starting from SfM points, 3DGS represents the scene with 3D Gaussians and employs fast splatting-based rasterization to accelerate both training and inference. SuGaR [40] extends 3DGS to surface reconstruction by associating 3D Gaussians with the mesh surface and jointly optimizing both the Gaussians and the mesh. PGSR [41] utilizes planar-based Gaussian splatting combined with unbiased depth rendering to maintain global geometric accuracy. To reconstruct complete surfaces in unbounded scenes, GOF [42] leverages a ray-tracing-based volume rendering, which enables a mesh to be extracted directly from the Gaussian representation. Furthermore, 2DGS [10] and Gaussian Surfels [43] argue that the multi-view inconsistency inherent in 3DGS compromises reconstruction quality. To address this, they replace 3D Gaussians with 2D surfels, enabling more precise capture of intricate geometric details. However, the discrete nature of Gaussian Splatting undermines the smoothness of reconstructed surfaces, particularly in regions with low texture or limited observational coverage. DN-Splatter [44] employs depth and normal priors to improve the smoothness of surface reconstruction based on 3DGS. Certain methods [15, 45] simultaneously learn an implicit Signed Distance Function (SDF) field alongside 3D Gaussian Splatting, utilizing the smooth SDF field to regularize the noisy geometry inherent in 3DGS. However, the mutual interaction between these components often compromises reconstruction quality, resulting in suboptimal outcomes. In contrast, our approach integrates the implicit field with Gaussian Splatting under the Atlanta-world assumption, enabling smooth surface reconstruction while preserving high-frequency geometric details.

3 Methods

Our goal is to reconstruct scenes with strong structural priors from posed images. To achieve this, we first introduce the preliminaries of 2DGS [10] in Sec. 3.1, followed by our implicit-structured representation in Sec. 3.2. To leverage structural priors under the Atlanta world assumption, we propose 3D global planar regularization and 2D local surface regularization, as detailed in Sec. 3.3. Finally, we describe the training process in our framework. An overview of our approach is illustrated in Fig. 2.

3.1 Preliminary

Instead of representing scenes with blobs as 3D Gaussian Splatting [39], 2DGS [10] models scenes as surfels distributed around the surfaces. Each surfel is defined in a local tangent plane in world space. To render images, 2DGS rasterizes surfels to the image plane with tile-based rasterization and Ray-Splat intersection to reduce depth bias. Then, 2DGS performs alpha blending [46] to get the rendered attribute A by composing these primitives sorted by depth:

SfM Points

Sparse Feature Grid

Semantic Decoder

GS Attribute Decoder

Rasterization

Semantic-lifted SfM Points

Plane Indicators Estiamtion

⨂

⨂

3D

3D

1

1

1

2D

Wall

Semantic GS Field

Rendering

GT / MonoPrior

RGB

Depth

Normal

Seg

Atlanta-world Guidance

Update

𝑓

!

𝑓

"

000-

T1

where A are the rendered 2D scene information (e.g., color c , depth d , normal n , semantics z ), a i and α i denote the 3D property and opacity contribution of i -th Gaussian primitive, and ∏ i -1 j =1 (1 -α j ) is the accumulated transmittance. Then, 2DGS optimizes these Gaussians with photometric loss and distortion loss to reduce floaters. The Ray-Splat intersection and well-defined normal introduce multiview consistency depth, providing better reconstruction quality. Though 2DGS produces plausible surfaces and models high-frequency details with explicit Gaussian representation, the discrete nature introduces discontinuities, which leads to broken or protruding texture-less walls.

3.2 Implicit-Structured Gaussian Splatting

In this section, we present our implicit-structured Gaussian representation, which leverages a sparse feature grid and implicit functions to organize discrete Gaussian primitives, ensuring locally coherent geometry while preserving high-frequency details.

Specifically, given the sparse point clouds generated from SfM [47], we first construct a sparse feature grid V = {V i g , V i s , { ∆ i k } K k =1 , l i } N v i =1 with a predefined voxel size, including geometry V i g and semantic features V i s , offsets of K local Gaussians { ∆ i k } K k =1 , scaling factor { l i } N v i =1 shared with local Gaussians. Given a voxel located in v , we deocde all Gaussian geometry attributes of K local Gaussians via corresponding geometry MLP M g ( · ) and semantic MLP M s ( · )

Here, d is view direction which assists in capturing view-dependent appearance, α ∈ R , s ∈ R 2 , q ∈ R 4 , c ∈ R 3 , and z ∈ R 4 refer to the opacity, scale, rotation, color, and semantic attributes of each Gaussian, respectively, whose positions p is defined as p i k = v i + l · ∆ i k . Then we render images with all these Gaussian attributes decoded from the sparse feature grid by surfel rasterization from [10]. In contrast to 2DGS [10], which optimizes Gaussians independently, our decoder predicts local Gaussian attributes, allowing each Gaussian's optimization to influence its neighbors and capture fine object details through the predicted Gaussian primitives. Consequently, the implicit-structured Gaussian framework combines the strengths of MLPs and Gaussians, achieving smooth local geometry while preserving high-frequency details.

Gaussian Semantic Lifting. To recognize structural regions in scenes, we lift 2D semantics to each Gaussian. We use the 2D pseudo-labels ˆ Z obtained by the pre-trained semantic segmentation model [48] as supervision and optimize the semantic Gaussians with the rendered semantic probability Z . To obtain the rendered semantics probability Z , we render the 3D semantic attribute z defined

in Eq. (2) into image space with Eq. (1), and optimize the semantic feature grid V s and semantic MLP M s by minimizing the cross-entropy loss:

where glyph[a0] ∇ [ · ] denotes the stop-gradient operator, used to prevent inconsistent supervision from distorting the geometry, and U denotes the set of training pixels in each iteration. Here, we define z ∈ R 4 , indicating wall, floor, ceiling, and others.

3.3 Atlanta World Guided Planar Regularization

Man-made indoor and urban environments typically exhibit rich structural information and conform to the Atlanta world assumption [18]. To leverage the globally consistent structural priors inherent to such scenes, we first propose learnable explicit plane indicators to effectively represent scene structural information, such as the ceiling and floor. We then introduce two types of regularization: 3D global planar regularization, which refines Gaussian positions and orientations to align with the plane indicators, and 2D local surface regularization, which provides positional supervision in poorly defined wall regions by aligning them with the plane indicators.

Explicit Plane Indicators The Atlanta world assumption [18] hypothesizes that such a scene can be approximated by a combination of a dominant horizontal plane, like the floor for indoor scenes and ground for outdoor scenes, and multiple vertical planes, such as walls and buildings. Based on this assumption, we define explicit plane indicators with a gravity direction and two distance offsets to represent the floor and ceiling of an indoor scene. Specifically, the floor plane and ceiling plane can be represented with π f = ( n g , d f ) , π c = ( -n g , d c ) , and their plane parametric equations are as follows:

where p ∈ R 3 is the location of 3D points, d f and d c are the distances from the origin to their respective planes, and n g is the gravity direction. For the urban scenes, we omit the ceiling plane.

To achieve this, we extract ceiling points and floor points, either from semantic Gaussians or semantic lifted sparse points. We then apply RANSAC [49] to regress the plane coefficients and determine the gravity direction to initialize plane indicators. Finally, we optimize the plane indicators alongside the Gaussians. Please refer to our supplementary for further details.

3D Global Planar Regularization. According to the associated probability distribution z ∈ R 4 , we can obtain the semantic label of each Gaussian. We enforce two critical geometric regularizations: normal alignment and planar constraint. The normal alignment enforces normals to be perpendicular to the gravity direction in wall regions while ensuring they remain parallel in ceiling and floor regions. Besides, the planar constraint ensures that the Gaussian positions p i lie in their corresponding ceiling or ground plane. Thus, the 3D global planar regularization is formulated as:

where M f , M c , M ∥ and M ⊥ are the floor, ceiling, parallel, perpendicular sets, p ∥ , p ⊥ , p c , p f are the corresponding probabilities, n i and p i are the normal and position of i -th Gaussian.

2D Local Surface Regularization. Previous methods [14, 11, 13] with implicit representation derive normals from the gradient of the given points, while optimizing normals can also optimize the

ScanNet

ScanNet++

ManhattanSDF

Scaffold-GS

2DGS

GSRec

Ours

GT

local surface. However, the Gaussian representation explicitly decouples the positions and normals of Gaussians. It poses a challenge that only optimizing Gaussian orientations in 3D space does not directly affect their spatial distribution. Without explicit positional regularization, wall Gaussians may not lie on the same plane, resulting in misalignment between the surface and the plane indicators.

To address this issue, we introduce 2D local surface regularization by regularizing the normal N d from the rendered depth D . With the semantic Gaussians, we obtain coherently rendered semantics and depth, from which we derive local surface normals in wall regions. Then, we align the surface normal with our plane indicators, optimizing Gaussian positions more directly. Besides, to mitigate misclassification introduced by the semantic segmentation model [48], we weigh the loss according to the probabilities. Thus, our 2D local surface regularization loss is formulated as:

where p c,f is the sum of the probabilities of the floor and ceiling, p w is the probability of the wall, P are the points backprojected from depth, ∇ is the gradient operator. So, our structural regularization loss is:

3.4 Training

We optimize Gaussians and structure with the following:

where L rgb is photometric loss proposed in original 3DGS, the distortion loss L dist and normal consistency loss L nc from 2DGS [10]. λ i , i ∈ { 1 . . . 6 } are the loss weight, L sem is the semantic

loss mentioned in Sec. 3.2. To provide the smoothness of local surfaces, we incorporate monocular geometry priors from pre-trained models [8, 53] for indoor reconstruction during training.

where N , ˆ N , ˆ D are the rendered normal, the normal prior and depth prior from [8, 53], respectively. w,q are the scale and shift computed from least-squares to align rendered depth D to monocular depth ˆ D .

4 Experiments

Implementation Details. We implement our approach in PyTorch [55], incorporating a custom surfel rasterization module for semantic learning, and optimize the parameters with Adam optimizer [56]. The hyperparameter K is set to 10, and the voxel size is fixed at 0.01. During training, the loss weights λ 1 , λ 2 , λ 3 , λ 4 , λ 5 , λ 6 are set to 0.25, 0.1, 0.1, 1.0, 100, and 0.05, respectively. The explicit plane indicator is initially derived from the semantic lifted SfM points and is reinitialized using the semantic Gaussians if the discrepancy exceeds a predefined threshold. Surfaces are extracted using TSDF Fusion [57]. All experiments are performed on a single NVIDIA 4090D GPU. Additional implementation details are provided in the supplementary material.

Datasets. Our method leverages the Atlanta world assumption, making it well-suited for both indoor and urban scenes. We evaluate our method using well-known datasets for both indoor and outdoor scene reconstruction. For indoor environments, we use ScanNet [50], ScanNet++ [51], and Replica [52]. For outdoor settings, we employ the MatrixCity [58] dataset for surface reconstruction. ScanNet and ScanNet++ feature large-scale RGB-D images and 3D surfaces akin to real-world scenarios, while Replica offers a synthetic dataset with high-quality images from 3D meshes. MatrixCity provides a synthetic, city-scale dataset for neural rendering. In line with previous studies [13, 14], we select four scenes from ScanNet, seven from Replica, and four from ScanNet++, sampling images

GaussianPro

GSRec

2DGS

Ours

GT

uniformly from the sequences. For outdoor evaluation, four city blocks from MatrixCity are used. Additional details are provided in the supplementary material.

Baselines. For indoor scenes, we compare our approach against two types of methods: 1) Neural implicit representations: ManhattanSDF [13] and MonoSDF [14]; 2) Gaussian-based representations: Scaffold-GS [54], 2DGS [10], DN-Splatter [44], and GSRec [17]. Additional supervision with monocular depth and normals, as noted in Eq (11) and Eq (12), is integrated into Scaffold-GS and 2DGS for indoor scenes. The quantitative evaluation for indoor scenes includes accuracy, completeness, precision, recall, and F-score. For outdoor scenes, comparisons are made with 2DGS, GSRec, Scaffold-GS, and GaussianPro [59]. In this context, the geometric evaluation relies on accuracy, completeness, and chamfer distance.

4.1 Comparison

Indoor Surface Reconstruction. We evaluate surface reconstruction on ScanNet [50], ScanNet++ [51], and Replica [52]. As shown in Tab. 1 and 2, our method outperforms both implicit and explicit baselines, achieving state-of-the-art results. SDF-based implicit methods like ManhattanSDF [13] and MonoSDF [14] ensure smooth surfaces but struggle with fine details, e.g., 'lamp' in orange box of Fig. 3) and suffer from multiview inconsistency in textureless regions, e.g., there is a discontinuity on the floor in MonoSDF.

Additionally, the time-consuming ray sampling in NeRF-based frameworks is to blame for the training time exceeding 7 hours and the inability to achieve real-time rendering. Gaussian-based explicit methods, such as Scaffold-GS and 2DGS, are fast but produce discontinuous surfaces due to

Scaffold-GS

2DGS

DN-Spaltter

GSRec

Ours

GT

ScanNet++

independent primitive optimization or view-dependent geometry. GSRec [17] improves geometry via IMLS but still yields noisy results. In contrast, our implicit-structured Gaussians combine locally coherent geometry with high-frequency detail preservation, enabling smoother and more accurate reconstructions. While slower than prior Gaussian methods due to decoding all Gaussians when rendering, our approach remains much faster than implicit ones and delivers superior quality.

Urban Surface Reconstruction. Structural priors are common in man-made environments, including both indoor scenes and urban buildings. To evaluate our method under such priors, we use the MatrixCity dataset [58]. We compare against GaussianPro [59], Scaffold-GS [54], GSRec [17], and 2DGS [10]. As shown in Tab. 3, our method yields more accurate and complete surfaces by leveraging the Atlanta world assumption. In Fig. 4, GaussianPro suffers from noisy surfaces and inconsistent depth despite normal propagation. GSRec reduces noise via IMLS regularization but produces sparse reconstructions in textureless regions, and its Poisson surface reconstruction trims low-density areas, resulting in missing geometry. 2DGS also struggles in textureless regions like the sea and building facades, leading to artifacts such as protrusions or holes. In contrast, our approach delivers smoother and more accurate surfaces in these challenging regions.

Novel View Synthesis. We evaluate novel view synthesis on the Replica [52] and ScanNet++ [51] datasets. As demonstrated in Tab. 4 and Fig. 5, our method achieves superior quantitative results, rendering photorealistic views with accurate geometry while avoiding the artifacts common in other approaches. While Scaffold-GS and 2DGS perform well on synthetic datasets without significant lighting variations, they struggle to render photorealistic novel views in real scenes. Scaffold-GS models significant lighting variations using view-dependent geometry, which can lead to overfitting in scenes with substantial lighting changes, such as those in [51]. This overfitting results in inaccurate lighting environment and geometry, as shown in Fig. 5. 2DGS [10] achieves a higher quantitative result on Replica with a discrete representation, however, the discrete representation exhibits protrud-

ing surfaces and results in noisy images on real scenes. GSRec [17] improves geometric accuracy but produces a blurry background and objects, lacking detailed modeling. In contrast, with its precise geometry, our method effectively models lighting variations across views while accurately capturing the appearance of the background and objects.

4.2 Ablation Study

(a) w/o

L

reg

(b) w/

L

reg

(c) GT

We conduct an ablation study on the ScanNet [50] dataset to evaluate the contribution of each component in our method, including the implicit-structured Gaussian, depth and normal priors, as well as the proposed 3D global planar and 2D local surface regularization terms. We report geometric evaluation metrics including Chamfer Distance (CD) and F-score (5 cm threshold), as summarized in Tab. 5.

As shown in Tab. 5, each component contributes positively to surface reconstruction quality. Compared to 2DGS (Row a) which uses normal and depth priors, our method with implicit-structured Gaussians (Row d) significantly improves geometry, as it provides locally coherent structures and better leverages the guidance from geometry priors. By comparing Rows b, c, and d, we observe that our 3D global planar regularization and 2D local surface regularization enhance geometry quality and yield superior geometric metrics. These regularization terms ensure globally consistent geometric supervision under the Atlanta world assumption, mitigating the inconsistencies and inaccuracies inherent in depth and normal priors. Furthermore, Fig. 6 illustrates the geometric comparison of our structural regularization, demonstrating straighter wall regions. Additionally, we perform ablations on the geometry priors themselves. As seen in rows e and f, removing either the normal prior or the depth prior leads to noticeable degradation, confirming that both types of geometric supervision are critical for high-quality reconstruction. Our full model (Row b) integrates all components and achieves the best performance with the lowest CD and highest F-score.

5 Conclusion & Limitations

This work presents a novel framework, AtlasGS , which reconstructs structured scenes using implicitstructured Gaussians under the Atlanta world assumption. We introduce our implicit-structured Gaussian representation as a hybrid approach that provides locally coherent geometry via MLPs and preserves high-frequency details through explicit Gaussian representation. To resolve the global inconsistency of geometric priors, we propose 3D and 2D regularization strategies based on the Atlanta world assumption, effectively correcting textureless regions. As a result, our method achieves fast and accurate surface reconstruction, and extensive experiments demonstrate that it achieves state-of-the-art performance. However, there are also some limitations in our work. First, the training and rendering speed are slower than the previous Gaussian-based methods [54, 10, 39]. Second, our method is primarily based on the Atlanta world assumptions, which depend on a pretrained semantic segmentation model for a limited set of elements. The future extension of our work is to speed up the training and rendering speed and facilitate SAM [60] and geometry priors to provide planar information, which can provide better performance and broader applicability.

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A Appendix

A.1 Planar Indicator Initialization

SfM Points

Side View

Perspective View As described in Sec. 3.3, we initialize the plane indicator using ceiling and floor points derived from semantically lifted SfM points or semantic Gaussian primitives. The SfM points are generated by triangulating posed images through 2D feature matching, establishing 2D-3D correspondences. Utilizing these correspondences, we aggregate semantic labels for each 3D point from 2D semantic maps across multiviews and apply a voting procedure to identify the most prevalent semantic label, including those for ceiling and floor. The Gaussian semantic lifting module, mentioned in Sec. 3.2, lifts 2D semantic maps to each Gaussian primitive, and each primitive contains a semantic probability of the wall, floor, ceiling, or other categories. Consequently, SfM points and Gaussian primitives are assigned structural semantic labels such as wall, floor, or ceiling, allowing us to extract ceiling and floor points.

Subsequently, we conduct plane fitting to identify the floor plane ( n f , d f ) using RANSAC [49] applied to the extracted floor points. The normal vector n f is chosen as the gravity direction n g . The offset of the ceiling plane, d c , is calculated based on the ceiling points and the gravity direction as follows:

where P ceiling represents the set of ceiling points. The plane indicator is initially determined using the semantic lifted points. If the angle deviation or the offset discrepancy surpasses a threshold, the plane indicator is reinitialized using semantic Gaussian primitives to minimize inaccuracies in textureless regions. Fig. 7 further illustrates plane indicators derived from both semantic lifted sparse points and semantic Gaussian primitives, demonstrating that both approaches can provide reliable structural priors.

A.2 Additional Implementation Details

Our implementation is based on PyTorch, utilizing customized surfel rasterization techniques for semantic learning. Parameters are optimized using the Adam optimizer. Most of the training learning rates are similar to those used in [54]. We set the hyperparameter K to 10 for indoor scenes and 5 for urban scenes, with a voxel size of 0.01, and the feature dim is 32 in our sparse feature grid. For all scenes, the implicit-structured Gaussian is trained for 40,000 steps. Voxels grow between steps 1,500 and 20,000, provided the gradients of the Gaussians exceed 2e-4 and are pruned if the opacities of all local Gaussians fall below 0.005. During training, we start our 3D global planar regularization from step 7000 and 2D local surface regularization from 20000. After completing training, surfaces are extracted using

TSDF-Fusion [57], following the approach described in [10].

A.3 Additional Exprimental Details

Similar to previous works for indoor scene reconstruction [14], we select four scenes in ScanNet [50], including scene0050_00 , scene0084_00 , scene0580_00 , scene0616_00 and seven scenes

in Replica [52], office0~office3 , room0~room2 , and as for ScanNet++ [51], we select four scenes, 8b5caf3398, b20a261fdf, f34d532901, f6659a3107 .

As described in Sec. 4, we uniformly sample images on the indoor scenes due to redundant images in the original dataset. For each scene in ScanNet [50] and Replica [52], we select one out of every 10 images in the original image sequence. For ScanNet++ [51], we use the image sequence from the iPhone and select one out of every 60 images. All the images are cropped and resized, and center-cropped to 640 × 480. For MatrixCity [58], we use all the provided images and make the image resolution 960 × 540. The SfM points are triangulated by COLMAP [47] with given images and corresponding poses.

A.4 Evaluation Metrics

Following previous methods [13, 14], we evaluate accuracy (Acc), completeness (Comp), Chamfer Distance (CD), precision (Prec), recall (Recall), and F1-score on ScanNet [50], ScanNet++ [51], and Replica [52]. Tab. 6 shows the definition of these metrics.

A.5 Additional Indoor Experiments

Semantic Segmentation. We evaluate the semantics from the rendered and the pre-trained segmentation model Mask2Former [48] on Replica [52] and ScanNet++ [51]. As shown in Tab. 7, ours achieves better results across all three classes on both datasets. By leveraging Gaussian semantic lifting, our model effectively aggregates multi-view information into 3D space and renders view-consistent semantic maps. In contrast, the 2D semantic segmentation model is more susceptible to image noise, leading to misclassifications, as illustrated in Fig. 8. The joint optimization scheme also helps correct semantic misclassifications, particularly around the boundaries between floors and walls.

A.6 Additional Qualitative Results

We present qualitative top-view results for ScanNet, ScanNet++, and Replica in Figs. 9 to 11, respectively. For additional comparisons, please refer to our accompanying video.

Methods

Replica [52]

ScanNet++ [51]

IoU

w

↑

IoU

f

↑

IoU

c

↑

IoU

w

↑

IoU

f

↑

IoU

c

↑

Mask2Former [48]

0.628

0.823

0.900

0.684

0.780

0.767

Ours

0.701

0.846

0.927

0.732

0.858

0.777

GT

Mask2Former

Ours

2DGS

Scaffold-GS

GSRec

Ours

ManhattanSDF

GT

2DGS

Scaffold-GS

GSRec

Ours

ManhattanSDF

GT

2DGS

Scaffold-GS

GSRec

Ours

ManhattanSDF

GT

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\n* For the reconstruction of indoor and urban scenes, a geometric prior that can properly restore low-texture regions is necessary, and a new representation is required that overcomes the limitations of GS and SDF.\n* First, a good geometric prior can be created by introducing the concept of the Atlanta world model. The Atlanta world model assumes that the vertical direction appears only in the direction perpendicular to the surface, and is applied to most indoor or urban scenes. Using this assumption, proper surface reconstruction is possible even in low-texture regions through regularization.\n* The authors proposed an implicit-structured Gaussian representation that can perform efficient rendering while preserving details effectively. By embedding an implicit voxel grid, both the accuracy and efficiency of surface reconstruction are improved.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n* The authors demonstrated higher reconstruction quality than existing 3D reconstruction models by utilizing the Atlanta world model, which well represents the characteristics of cuboid-shaped indoor rooms or urban cities consisting of buildings.\n* The authors stated the limitations of existing 3D representations and proposed a mixed version called implicit-structured Gaussian splitting.\n\n## Weaknesses\n* The prime concern of the work is the limited scope of the proposed model.  As stated in the paper, since this method is based on the Atlanta world model assumption, it cannot be applied to more complex scenes with complicated objects. When experimenting with a dataset where the Atlanta assumption is valid, it is trivial to observe that the proposed method outperforms existing methods. However, the scenarios to which this assumption is applied are quite limited, and the performance of AtlasGS may be inferior to existing models in real-world scenarios. The author should demonstrate that the model can achieve comparable performance to more general scenes with complex objects, thereby validating the contribution of the work.\n* In implicit structured GS, is a Gaussian predicted for every voxel in a predefined voxel grid? If so, what is the total number of voxels ($N_v$) and the number of GSs estimated for each voxel ($K$)? If a fixed number of GSs is created for every voxel instead of estimating valid anchors (Scaffold-GS), it will generate many redundant Gaussians and become memory-inefficient. This can be seen by comparing the memory cost difference between AtlasGS and other models.\n* How are the values of depth and normal calculated? Are they computed through alpha-blending, as in Equation 1? If so, the rendered depth and normal vector might be vulnerable to the attribute of Gaussian splats in the back, which should not be considered in real measurements.\n\nQUESTIONS:\nIn Equation 10, many hyperparameters are used to calculate the loss. I wonder if there is an analysis on how the performance changes depending on the value of each hyperparameter. Also, $\\lambda_{sem}$ in L202 should be changed to $\\mathcal{L}_{sem}$.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe task of this paper is to reconstruct 3D scenes from multi-view images for indoor and urban scenes. This paper points out two problems in current task: 1) The geometry priors is not global consistent while handling low-texture regions, like planes(floors/ceilings/walls) in the scenes. 2) Gaussian Splatting and implicit SDF filed methods either suffer from discontinuities and inefficient issues. To handle these issue, this paper proposed an Atlanta-world guided implicit-structured Gaussian Splatting which can achieve smoother reconstruction results while preserving high-frequency details and rendering efficiency. Experiment shows that the proposed method well alleviates the problems both qualitatively and quantitatively.\n\nIn conclusion, the problem presented in this paper is important for 3D reconstruction, and the proposed scheme is effective. I think it can bring inspiration for the reconstruction community. But due to the weakness and questions comments. I decide to give an border reject for this paper. But I feel open to adjust the score from 3 to higher if the author gives a more clearer response.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths*\n1. The studied problem is important, the technical details are complete and clearly written which I think can bring insights for the reconstruction community.\n2. The proposed method is simple but effective, resulting good in both qualitative visualization results and quantitatively geometry metrics.\n\nWeaknesses*\n1. The problem is not clearly presented. According to Line 25 and 27, this paper have mentioned two problems, global inconsistency for monocular geometric priors and failure in not mutually orthogonal structures for Manhattan-world assumption. But according to Figure 1, this paper use 2DGS as baseline to present this problem, but did 2DGS use geometric priors or Manhattan-world assumption? I have searched the 2DGS paper[1], but there are not ‘prior’ or ‘Manhattan’ word in the paper. So Did the problem present by Figure 1 stem at these prior? Actually, I only realize the discontinue issue in ScanNet part in Figure 1.\n2. In the experiment, this paper only present the geometry metrics, but how about the semantics metrics after reconstruction like PSNR/SSIM/LPIPS and how about the qualitative results of the semantics reconstruction? Further, can you provide the training time and FPS in Table 3 in urban scenes like Table 2? \n3. The rendering efficiency of the scheme seems affected a lot by the newly proposed component.\n\nQUESTIONS:\n1. I have seen an another work for urban scenes reconstruction[2], they also achieve a great results. What is difference or strength between your work and [2]?\n2. Can the latest work VGGT[3] handle the problem of “Traditional multi-view stereo methods struggle on these textureless surfaces due to the lack of distinctive” in Line 22?\n3. Was the “plane indicators” mentioned many times the same as  “plane normal”?\n4. In Line 253, you mentioned that the scheme need to decode gaussians online? Why need online operation? Isn't gaussians fixed after it has been trained? Further, how about the total GPU memory usage during inference in indoor/urban scenes and how about the additional GPU memory increased by the online decoding operation?\n5. Can the proposed scheme used to reconstruct the static scenes in autonomous driving scenario like nuscenes and waymo?\n6. In which case the proposed scheme will fail?\n\nLIMITATIONS:\nSame with weakness", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a Gaussian Splatting method for reconstructing a man-made environment with high surface quality, utilizing the Atlanta-world assumption to constrain and guide the scene reconstruction process. \n\nThis paper first constructs a sparse feature grid on top of the SfM points, allowing the attributes (also the semantic features) of the Gaussians to be decoded from it. The semantic feature for representing planar structures is learned with supervision from a 2D semantic segmentation model.\n\nThen comes the Atlanta World Guided Planar Regularization, in which the planar region is explicitly indicated with RANSAC, then used for regulating the plane constraint with the proposed loss functions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe methodology of this paper is simple, straightforward, and presented in a clear way. The following experiments on indoor and outdoor datasets showcase the effectiveness of the proposed method. Discussions about the training time and rendering FPS without concealing are also considered a plus mark.\n\nWeaknesses:\nAtlanta World assumption is, from my point of view, would break in many cases, e.g., indoor space at the roof floor where the ceiling is not parallel with the ground, a cinema or theater where the ground steps down, a modern design art museum with complex non-plane structured ceiling and even walls which is not plane at all. In that case, the 3D Global Planar Regularization may not stand. A mechanism for dealing with such a situation is not explored in this paper.\n\nQUESTIONS:\n1. I failed to find details about how the predicted 3D mesh is generated from a trained 3D Gaussian Field, whether all Gaussian Splatting-based methods mentioned in the experiments are processed with the same meshing method, or not?\n\n2. The experiments are conducted by evaluating the global geometric reconstruction accuracy. I would like to see the geometric accuracy of non-planar surfaces using AtlantaGS, like running the same evaluation but removing walls, ceiling, and ground floor. From the qualitative result, I see some cases, the constrained planar region also brings better reconstruction quality of other objects nearby the plane. But quantitative results will be even better.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes to address the gaussiona-splat-based surface reconstruction problem with the assumption of the Atlanta world, for achieving better continuity for texture-less area as well as richer high frequency details. This is achieved by incorporating a sparse-volume-grid-based surface representation built on top of the 2DGS representation while maintaining neighbourhood modeling and semantic guidance. Furthermore, to better leverage the Atlanta world assumption, two additional regularization terms are further imposed for failure imposition of the assumption. The experimental results demonstrate overall good results both quantitatively and qualitatively. The paper is well written and executed and clear for reading.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n\nThe paper points out the insufficiency of existing GS-based surface model, in particular like 2DGS with the collapsed ellipsoid onto the surface that leads to discontinuity and lack of details of the surface. These are valuable observations that the authors are motivated to propose their new representation, in particular, a sparse-volume-grid for rich representation power while maintaining budgeted memory footprint, and the MLP “head” for smooth local neighbourhood modeling. While such hybrid feature form is observed also in the NeRF-related literatures, they are nonetheless new in this context. \n\nThe authors proposed to impose semantic / attribute predictions that would potentially aid the imposition of the Atlanta world assumption. The proposed regularization terms for maintaining consistent geometry among textureless area are neat, and the overall learning pipeline can lead to promising and high quality reconstruction results.\n\nThe authors have conducted with efforts a set of experiments, showcasing that the proposed optimization algorithm can indeed demonstrate faithful local geometric results, especially in those areas where otherwise holes (discontinuity) or irregular / overly smoothed geometric textures presented. Quantitative results on geometric evaluation shows promising outcomes. The pixel reconstruction quality, as expected, occasionally not the best, as the main focus and model design are toward the faithful reconstruction of the geometry. The overall results are convincing.\n\nWeakness\n\nAs what the primary goal of this study presented, the proposed approach would be mostly suitable for scenes with the Atlanta world assumptions, meaning the performance might get some level of degradation on datasets otherwise. I am actually pretty curious regarding whether the proposed model could maintain geometric accuracy over scenes without such assumptions, and which of the new components shown in this paper could correspond to the potential improvement on non-Atlanta scenes? (the volume grid?) \n\nWhile the authors have presented with sufficient arguments regarding their improvement over 2DGS (models that collapse ellipsoid onto the surfaces), the authors nonetheless seemed to provide very brief discussions about to-what-extent that the addressed issues in the context of papers where the Gaussians are regularized to the mesh (e.g. [36]) or an SDF is attached [10]. Are those branches of works can also cope with surface continuity over textureless areas or providing rich geometric textures? Is the volume grid presented in the model that contributes most to the geometric details while the MLP contributed to the continuity (plus semantic guidance)? The relations between what the model has improved and what is proposed would be better elaborated.\n\nQUESTIONS:\nPlease address my questions presented in the \"weakness\" section above.\n\nLIMITATIONS:\nYes (mostly). \n\nOne minor (that might be pressumed to be - so no need for explictly listed) - the proposed approach primarily works for scenes with the Atlanta world assumption.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-07"}