Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting
Authors: Chuandong Liu
Huijiao Wang
Lei YU
Gui-Song Xia
Venue: NeurIPS 2025 poster ()
Keywords: Gaussian Splatting
Large-Scale Scene
3D Reconstruction
Novel View Synthesis
Primary area: applications

Abstract: Recent advances in 3D Gaussian Splatting have shown remarkable potential for novel view synthesis. However, most existing large-scale scene reconstruction methods rely on the divide-and-conquer paradigm, which often leads to the loss of global scene information and requires complex parameter tuning due to scene partitioning and local optimization. To address these limitations, we propose MixGS, a novel holistic optimization framework for large-scale 3D scene reconstruction. MixGS models the entire scene holistically by integrating camera pose and Gaussian attributes into a view-aware representation, which is decoded into fine-detailed Gaussians. Furthermore, a novel mixing operation combines decoded and original Gaussians to jointly preserve global coherence and local fidelity. Extensive experiments on large-scale scenes demonstrate that MixGS achieves state-of-the-art rendering quality and competitive speed, while significantly reducing computational requirements, enabling large-scale scene reconstruction training on a single 24GB VRAM GPU.

Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting
Chuandong Liu²
Huijiao Wang Lei 2u2,3,
Gui-Song 21,5x
'School of Computer Science, Wuhan University
Schooo of Artificial Intelligence, Wuhan University
School of Electronic Information, Wuhan University
Sttte Key Lab. of LIESMARS, Wuhan University
Institute for Math & AI, Wuhan University "Corresponding Authors
Project Page: mixgs. boogs.githube github.
Abstract
Recent advances in 3D Gaussian Splatting have shown remarkable potential for novel view synthesis. However, most existing large-scale scene reconstruction methods rely on the divide-and-conquee paradigm, which often leads to the loss global scene information and requires complex parameter tuning due scene par- titioning and local optimization. To address these limitations, we propose MixGS, novel holistic optimization framework for large-scale 3D scene reconstruction. MixGS models the entire scene holistically by integrating camera pose and Gaus- sian attributes into view-aware representation, which is decoded into fine-detailed Gaussians. Furthermore, a novel mixing operation combines decoded and original Gaussians jointly preserve global coherence and local fidelity. Extensive ex- periments on large-scale scenes demonstrate that MixGS achieves state-of arr rendering quality and competitive speed, while significantly reducing computa- tional requirements, enabling large-scale scene reconstruction training on single 24GB VRAM GPU.
Introduction
Accurate 3D scene representation is fundamental for wide range of applications, including aerial surveying [4], autonomous driving [26, 66], environmental monitoring [33, 65]. and AR/VR [18, 20], all of which demand high-fidelity visual quality and real-time rendering. recent years, implicit representations such as neural radiance fields (NeRF) [40, 63, 53] have achieved impressive results in novel view synthesis, but often suffer from limited detail reconstruction and slow rendering speed. More recently, explicit representations like 3D Gaussian Splatting (3DGS) [22] have demonstrated superior performance in both visual quality and computational efficiency, enabling real-time rendering and showing strong adaptability to dynamic scenes [62, 67] and content generation [75, 28).
Nevertheless, directly applying naive 3DGS to large-scale scene reconstruction faces significant challenges, such as out-of-memory issues and degraded novel view synthesis quality [30]. To address these limitations, most existing 3DGS-basse methods [34, 10] adopt divide-and-comee -ndn-quee strategy, inspired by its success in previous bbRe- approaches [52, 53]. As illustrated in Fig. 1 (left), the large scene partitioned into multiple independent blocks, enabling parallel training and optimization across multiple GPUs. After optimization, these blocks are merged to produce the final reconstruction for novel view rendering. Various improvements, such as progressive partitioning [30], recursive scene splitting [10], and adaptive data selection [34], have been proposed to enhance
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Other Methods
Our
mumber of blocks: 6, 9, 20?
visiblitty thresholdk 0.1, 0.6
civide
intersection threshold: 0.2, 05?
Complex Paranster Tuming
Bock
Nock
X Global structure inconsistent:
Caussarsinvvie
Deoded Gaussians
b
Mixing
bodk
block
Optimize & Marge
Mock
HockO
Local part optimized
render
Loss of Global Informtionn
Mixed Gaussians
Okund I-50TTeis SOGRR
Figure 1. Left: Conventional divide-and-conquee approaches partition the scene into multiple independent blocks, each optimized and rendered separately before merging. This strategy introduces major limitations: (1) complex parameter tuning, such as selecting number blocks and adjusting thresholds (e.g., visibility and intersection [30, 34J, which often requires extensive manual intervention and scene-specific reconfiguration, (2) loss of global information, leading to inconsistencies in glohal structure, illumination, and geometric continuity across block boundaries, as local optimizations do not guarantee global coherence. Right: In contrast, MixGS framework treats the entire scene and the Gaussian Representation network as holistic optimization problem. By extracting and encoding visible Gaussians, decoding new Gaussians via implicit feature representations, and mixing them with the originals, MixGS achieves both global consistency and fine-grained detail reconstruction.
the representation capability of Gaussian primitives within each block. Despite these advances, divide-and-conquer methods still suffer from major issues.
On the one hand, scene partitioning heavily relies on prior knowledge and manual parameter tuning. The choice of division intervals along the x, y. and æ axes determines the number and size of blocks, which significantly affects the convergence behavior and reconstruction quality. Besides, assigning appropriate training images each block also requires additional threshold-based adjustments, such as visibility [30] and intersection [34] thresholds, and these configurations are often scene-specific, necessitating re- re-tuning for different environments.
On the other hand, independently optimizing each block can lead to locally optimal but globally sub- optimal solutions, resulting in the loss of global scene consistency. For instance, lighting conditions that should uniform across the scene may become inconsistent, and continuous structures like buildings grasslands suffer from geometric discontinuities and texture misalignments at block boundaries due to fragmented optimization.
To address the aforementioned limitations of the divide-and-conquer paradigm, i.e., complex pa- rameter tuning and loss of global information, we propose MixGS, novel BOGS-based pipeline for large-scale scene reconstruction. Unlike traditional approaches that independently optimize and densify each scene block, as shown in Fig. (right), MixGS treats the entire large-scale scene and the Gaussian Representation network as holistic optimization framework. Specifically, Gaussian primitives within the visible frustum are first extracted based on camera poses and further filtered using opacity information. The positions of these selected Gaussians are encoded via 3D hash encoding to obtain spatial feature representations, while other Gaussian attributes and camera poses incorporated as auxiliary inputs enrich the feature space. These features are then decoded to generate refined Gaussians, with a designed offset pool. The decoded Gaussians are mixed with the original filtered Gaussians to form set of mixed Gaussians for rendering, where the original Gaus- sians primarily capture coarse scene structure and the decoded Gaussians compensate for missing fine-grained details. This holistic design not only alleviates the high GPU memory requirements by transforming explicit Gaussian densification into implicit high- nigh-dimensional feature encoding, enabling training single GPU, but also achieves rendering speeds comparable to other methods.
To comprehensively evaluate our approach, we conduct extensive experiments on two prominent benchmarks [31, 53] for large-scale 3D scene reconstruction, encompassing four challenging scenes.

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Our results demonstrate that MixGS significantly improves reconstruction performance in these scenarios. To sum up, our key contributions are follow:
We introduce MixGS, a novel holistic optimization method designed to overcome the limitations of existing approaches that rely on divide-and-conquer strategies for large-scale scene reconstruction. We develop mixed Gaussians rasterization pipeline that simultaneously captures global and local scene information through view-aware Representation modeling for implicit feature learning. Extensive experimentation demonstrate that MixGS achieves state-of-the-an results while maintain- ing comparable rendering speed on established benchmarks for large-scale scene reconstruction.
2 Related Work
Neural Radiance Fields. Neural radiance fields [40] (NeRF) revolutionized 3D scene representation by learning continuous volumetric function that maps spatial coordinates to color and density values through ray sampling. This breakthrough has inspired numerous follow-up workss [6. 16, 69] that address its computational and representational limitations. Recent advances have focused on improving training efficiency and rendering quality through various scene encoding strategies, includ- ing sparse voxel representations [32], multi-resolution hash tables [42], and orthogonal axis-plane decompositions [43]. These approaches have significantly enhanced NeRF's practical applicability while maintaining its high-quality rendering capabilities. Beyond per-scene reconstruction, significant progress has been made in generalizable NeRF approaches [7, 21, 36, 51, 57], which aim to transfer learned priors across different scenes. These methods often incorporate additional supervision signals, such as depth information [12, 47, 60], to improve training stability and reconstruction quality. To address the inherent aliasing artifacts in NeRF, researchers have proposed various solutions, including cone tracing approximations with scale-aware positional encodings [1, 2] and hexagonal sampling strategies [42, 3]. For large-scale scene reconstruction, recent works have explored the alignment of multiple NeRF blocks through both traditional optimization techniques [17] and geometry-ave transformers [9] pretrained on 3D datasets.
3D Gaussian Splatting, 3D Gaussian Splatting (3DGS) [22] represents a paradigm shift from NeRF's volumetric rendering approach, offering more efficient explicit representation through differentiable rasterization of 3D Gaussians. While 3DGS achieves real-time rendering performance, it faces challenges in identifying and optimizing Gaussian primitives, particularly in textureless regions. Recent works have addressed this limitation through various innovative approaches. Scaffold-GS [38] introduces a sparse voxel grid representation to encode Gaussian characteristics, effectively reducing unnecessary densification while maintaining scene fidelity. Further advancements include implicit scene encoding through graph neural networks (SAGS [55]) and hierarchical level-of-detail structures (Octree-GS [45]). The aliasing issue, common to both NeRF and 3DGS due to fixed-window Gaussian kernels during rasterization, has been addressed in Mip-Splatting [70] and subsequent works [29, 50, 64, 5]. Additional research directions include 2D Gaussian [19] learning for improved surface fitting [35], dynamic scene modeling through deformation learning [62, 67, 56]. and model
compression through quantization techniques [14, 58).
Large-Scale Scene Reconstruction. Large-scale scene reconstruction has evolved significantly from early Structure-from-t from-Motion (SfM) approaches [49, 48], which relied on traditional keypoint extraction methods like SIFT [37]. The availability of camera poses has facilitated scene partitioning. making the divide-and-conquee strategy mainstream approach for large-scale reconstruction. This paradigm has been successfully adopted in NeRF-based methods [25, 44, 54, 63, 73], with notable contributions including Block-NeRF [52], which incorporates appearance embeddings [39] and learnable pose refinement for handling environmental variations. Switch-NeRF [74] and Mega- NeRF [53] further enhance this approach through switch transformer-base ray assignment and network sparsity optimization, respectively.
The recent adoption of 3D Gaussian Splatting [22] has brought new possibilities to large- scale reconstruction, offering an optimal balance between visual fidelity [24, 13, 71, 61] and rendering strategy from NeRF, methods like CityGaussian [34] and VastGaussian [30] have developed specialized blocking strategies for 3DGS. These approaches ensure optimal training image distribution within blocks, enabling efficient techniques such as Level of Detail (L.OD) [23, 35, 11]. DOGS [10] introduces recursive splitting approach for balanced block sizes, coupled with distributed training for accelerated optimization.
speed [8, 15, 46). Following the successful divide-and-conee
3

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While these methods have demonstrated impressive progress in large-scale scene reconstruction, they still face two fundamental challenges: the need for complex parameter tuning and the potential loss of global scene consistency, which our work aims to address.
3 Approach
3.1 Problem Definition and Formulation
3D Gaussian Splatting [22] explicitly represents a static scene as a set of anisotropic 3D Gaussians [G+) offering both high visual fidelity and efficient training. Each Gaussian kernel is parameterized by its center 14: E R and covariance matrix Σg E R3x3, where indexes the i-th Gaussian. The 3D Gaussian function at point R is defined as:
Gx(x) = (x--)=)² ((x-p:)
where the covariance matrix D; is further factorized as Σ; = R,S.ST with R3 E 3x3 as the rotation matrix and S E R3x3 as the scale matrix. To render an image from the 3D Gaussian repre- sentation, the Gaussians are transformed from world to camera coordinates via view transformation matrix W E 3x3 and projected onto the 2D image plane using a Jacobian matrix The covariance matrix in the camera plane is computed as E' = JWEW J Given pixel on the image plane, the final color rendered by compositing the splatted Gaussians from front to back, following the conventional volume rendering equation:
3x33 [68, 76].
i-1
C(p) = = c [I az).
j=1
where c; is the color of the i-th Gaussian (computed from spherical harmonic coefficients), and or is the opacity associated with G.. Each Gaussian G, thus parameterized as (Jus, Qú Ti, 86, Gs), where is the quaternion corresponding to R2 8; is the scale vector (the diagonal of S.), and is the color.
Based on the above concept, for small-scale scene reconstruction, Gaussians can be directly optimized via the simplified objective function:
min Σ TF)
where G denotes the set of Gaussians that need to be optimized, 1 is the set of ground truth images, T; represents camera poses, M is the total number of training images, is the rendering process, and C is the loss function. As defined in [22], L = L1 + ALSSIM. However, directly training large-scale scenes with naive 3DGS is limited by memory constraints.
To address the limitations of naive 3DGS for large-scale scene, most existing methods adopt the divide-and-conquee strategy [53, 34). Specifically, the set of Gaussians G is partitioned into n blocks, i.e., G {Gb1,... }. and each block is assigned a corresponding subset of training images: 1st - Um, ]m Next, each block is optimized independently:
Divide-and-conquee schema:
Ma;
Gh
min Ghy
c (T)
Vbj E bb))
where Moj denotes the number of training images for block bj. Finally, all optimized blocks merged to produce the final set of Gaussians for rendering. While divide-and-on -nd-uuuue methods have achieved success through sophisticated partitioning strategies, they still face two major limitations: complex parameter tuning and loss of global scene information.
Our MixGS schema: In contrast, we propose a holistic optimization framework that jointly models the entire scene, aiming to design an objective function capable of optimizing large-scale scenes in the same manner as small-scale ones. Specifically, we pretrain coarse Gaussians Ge from the original large-scale scene and employ a view-aware representations to model the feature attributes under the current viewpoint. Further, the representations with learnable parameter & can be decoded to

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Camera Pose
Scale Fest.
Rotate Fest.
Prse Fest
concalmation
dot prodett
Feed Postams
Auxliary Fest. fug
Caut+ Careoins
MLP
y
Decoder
Spatial Foa h,
Fusion Feet Ags
Coarse Gaus,
Gaus. in View Prustum Fo
Hash Encoding fhash
F
Offset Pool Oe
Loss
Image
Rendered Image
Mixed Gaus. Go
Shifted Gaus, Go
Demded Gaus. Go:
Figure 2. Overview of the proposed MixGS method pipeline. We first train the original Gaussians to capture the coarse information of the scene. Then, based on the camera poses, we extract the Gaussians within the view frustum, thereby enabling implicit feature extraction that integrates Gaussian attributes with camera poses. These features are decoded using tiny multi-head MLP generate decoded Gaussians. Next, the positions of Gaussian primitives are adjusted via an offset pool and combined with original Gaussians to form the mixed Gaussians. Finally, the mixed Gaussians are splatted through the diffcrentiable rasterization operation [22] to render images for supervision.
Gaussian primitives Go enriched with scene details, which then mixed with the coarse Gaussians for final rendering. To sum up, our approach can be formulated as the following objective:
M
g: Φ min Go
L (E(Ge U Go. m).
It is casy to see that we are able to optimize large-scale scenes holistically in the same manner as small-scale scenes. Besides, we achieve high-quality rendering that captures both global structure and local details, thereby eliminating the need for complex parameter tuning and mitigating the loss of global information, while enabling training on single 24GB consumer-grade GPU. The following sections detail the design and holistic optimization of Gaussians G. and representations obtained from learnable parameters +
3.2 View-Aware Representation Modeling
Coarse 3D Gaussians Training. To facilitate subsequent feature extraction for different attributes of Gaussians, necessary first capture the coarse geometry and texture information of the large-scale scene. Following the standard 3DGS pipeline [22], we optimize all Gaussians using images and initial point clouds from COLMAP [48]. The resulting of trained Gaussian primitives is denoted as G. {G; , Nc), where N₂ represents the total number of Gaussians. This coarse representation provides a strong geometric prior for further attribute learning.
Multi-RResolttonn Hash Encoding. Given coarse Gaussians G., we extract the subset G. = [G;),, J.N, within the current view frustum, i.e., Go E Gc. determined by the camera pose T; (see in Fig. 2). The mean positions {p+ = (I Vo. 2,) ) of these Gaussians are used for spatial encoding. We employ multi-resolution hash encoding [42] capture geometric structure at multiple scales:
/==1
In Niax In Nmin
N = Nuiu b'].
exp
(6)
L-1
where Nmin and Nmax represent the coarsest and finest resolutions, respectively, L. denotes the largest number of levels, and 1 the index of the current level. The selected resolution N, determines the grid voxel resolution at each level. For given input k, the corresponding voxel position on the grid
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can then be computed via rounding down and up k [k values. positions are hashed into the corresponding hash table using the following hash function:
Nr., [ki] = N.]. To retrieve voxel
hy (k:) (e mod T,
where @ denotes the bit-wise XOR operation, } are distinct large prime numbers assigned to each dimension, d represents the input dimensionality, and TT' indicates the hash table size. The encoded features are then obtained through trilinear interpolation over the corresponding grid voxel values.
Generally, we denote the above hash encoder as Shash, the final spatial features h, are obtained via:
h Soz Shash (Vi, Dis zi),
(8)
where fa, is a tiny MLP with parameters 0,. We adopt multi-resolution hash encoding for its hierarchical structure, which enables progressive learning from coarse to fine details for performance gains, and for its efficient implementation [41], which ensures fast training and inference.
Auxiliary Gaussian Enhancement. To further enhance representation capacity, we independently encode the rotation quaternions r. scale vectors of Go. and the camera pose t; capturing local geometric variations and viewpoint-dependen effects. These auxiliary features are concatenated to form ha and transformed into attention scores, which modulate the spatial features:
hgs = (2- offo (ha)) - 1) @ fig,
where hgg is the fused feature, fe, is lightweight MLP, @ denotes element-wist multiplication, and is the Sigmoid function.
3.3 Mixed Gaussians Rasterization
In this section, the primary objective to decode the high-dimensional feature hyss which models the view-aware Gaussian attributes, thereby generating corresponding Gaussian attributes {1,, a,r, 8,8,0 c} as described in Sec. 3.1. Note that we call the generated Gaussians as decoded Gaussians Gg. In detail, we use multi-head MLP fe, to decode the features h1s and predict the Gaussian opacity R. color č E R², and covariance matrix Σ, which is reparameterization by rotation quaternion F E R. and a scaling matrix 5 E R.3. This process can be formulated as:
{(āg, Pr, Se, N foz (hge).
(10)
Note that the number of decoded Gaussians are the same with Gaussians in the view frustum, i.e. No = N₁. Before being used to formulate Gaussian representations, the rotation quaternion should be normalized and the opacity should be activated by Sigmoid function, to satisfy their range and feature. Moreover, we design offset pool O. = {01, ..,..., ON.. J to save the shifted value for each decoded Gaussian primitives. Further, the position of decoded Gaussians are calculated as:
(UN, + oNo.)
(11)
where Oz E Og is the offset pool in view, and uN. is the position attribute of Gaussians Go- Compared to directly predicting absolute positions, when the coarse Gaussian position is already close to the target structure, fine-tuning through local offset prediction can more effectively capture small-scale geometric variations. By predicting residual deviations rather than complete positional information, the model converges more easily and achieves greater precision in detail refinement.
With the aforementioned decoded Gaussians Ga. novel view can be rendered using the rasterization splatting pipeline [22]. However, the obtained view easily focus on view-aware local details, thereby lacking stable global structure. Hence, we propose to combine Gaussians Go and Go. generating mixed Gaussians GA for further rendering. This process can be formulated as:
G. G, U. (Gi, GN., GN,+1,-...,0 GNo........
(12)
Therefore, we can obtain final rendered view via the mixed Gaussians. this way, Gaussian densification is implicitly achieved, which facilitates the reconstruction of fine details and complex lighting in large-scale scenes. On the other hand, it significantly reduces the memory requirements during training, enabling efficient training even consumer GPU with 24GB of VRAM.

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3.4 Training with Multi-Stage Optimization
To ensure effective optimization during training, we adopt a three-stage optimization strategy. This approach progressively refines the model from both global and local perspectives, enabling better balance between global structure and local details in large-scale scene reconstruction.
(1) Coarse Stage. Coarse Gaussians Ge are trained rapidly capture the global geometric structure, providing stable prior and preventing premature overfitting to local details.
(2) Detail Stage. With coarse Gaussian parameters fixed, we optimize other parameters 4, such the hash encoder, MLP with learnable parameters, and offset pool, i.e., = ( f frash Oc), allowing the model to fit local details and high-frequency geometry based on the global structure.
Joint Stage, After capturing both global and local information, we jointly fine-tune all parameters (Ge and h)) using the mixed Gaussians. This stage integrates global and local cues, eliminates boundary artifacts, and leverages priors from earlier stages, further enhancing overall quality.
4 Experiments
4.1
Experimental Settings
Datasets and Evaluation Metrics. Following state-of-the- methods [10, 34], we conduct experi- ments on large-scale scenes across two real-world urban scene datasets: UrbanScene3D [31] dataset with Residence and Sci-Art, and Mill19 [53] dataset with Building and Rubble. Both of datasets consist of thousands of high-resolution images captured by drones. For fair comparison [10, 53, 30), we adopt the same data splits as previous works to construct the training and testing sets. Specifically, the Building, Rubble, Residence, and Sci-Art scenes contain 1920, 1657, 2582, and 3620 training images, respectively, and 20, 21, 21, and 21 testing images. For the evaluation of novel view synthesis, report PSNR, SSIM [59], and LPIPS [72] metrics. Additionally, measure Frame Per Second (FPS) to assess model effectiveness.
Compared Methods. We compare MixGS against seven Sfute-of-the-are methods, categorized into NeRe-based methods: Switch-NeRF [74]. Mega-NeRF [53], GP-NeRF [63] and 3DGS-based
methods: VastGaussian [30], CityGaussian [34], Hierarchy-GSS [23]. DOGS [10]. To ensure fair
comparison, we downsample all images by 4 times. Note that DOGS [10] differs slightly from other methods, which downsamples images by a factor of 6 times. Therefore, we also conduct experiments with times downsampling for direct comparison, as reported in the Appendix.
Implementation Details. All experiments are conducted on an NVIDIA RTX 3090 GPU with 24GB VRAM using PyTorch. Following previous methods [34, 10], we use the official camera poses provided by Mega-NeRF [53] and initialize 3D Gaussians with COLMAP [48]. The three training stages are run for 30,000, 40,000, and 260,000 iterations, respectively. To fit the original 3DGS [22] for large-scale scene reconstruction. we train for 60,000 iterations, applying densification every 200 steps until 30,000 iterations. VastGaussian [30] applies color correction rendered images before metric evaluation, which can significantly improve results. For fair comparison, we report the results of VastGaussian using a reproduced version [10] that excludes both color correction and decoupled appearance encoding.
4.2 Main Results
Quantitative results. Table reports the mean PSNR, SSIM, and LPIPS metrics across the four large-scale scenes. MixGS achieves the highest SSIM across all scenes, highlighting its superior perceptual quality. In terms of PSNR and LPIPS, our method consistently delivers either the best or highly competitive results. Notably, on the challenging Residence dataset, MixGS achieves PSNR of 23.39, enabling 3DGS-base based approaches to outperform NeRF-based methods. It is worth mentioning that superior PSNR values are obtained by NeRF- methods on the Sci-Art dataset, which exhibits inherent blur due to capture conditions. This can attributed to the tendency of NeRF-baase reconstructions to produce smoother outputs that align with the dataset's characteristics. In contrast, 3DGS -based methods, including our MixGS, maintain better structural and perceptual fidelity across all scenes, reflected in the SSIM and LPIPS scores.
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Table 1. Quantitative results of method compared previous work Mill19 dataset [53] and 3rbanScene3D dataset [31]. report metrics PSNRt. SSIM1, and LPIPS] on test views. The best, second best, and third best results are highlighted in red, orange, and yellow respectively. represents without applying the decoupled
appearance encoding.
Scenes
Building
Rubble
Residence
Sci-Art
Metries
PSNR
SSIM
LPIPS
PSNR
SSIM
LPISS
PSNR
SSIM
LPPS
[SNN
SSIM
LPIPS
Moga-NRRF [53]
0.553
Swith-NeeRF [74]
21.54
0.579
0.397
24.31
0.562
0.488
22.57
0.654
0.352
26:51
0.795
0.271
GP-NeRF [63]
21.03
0.566
0.486
21.06
0.565
0.196
22.31
0.661
0.118
25.37
0.783
0.373
3DGS [22)
20.46
0.720
0.305
25.47
0.777
0.277
21.4
0.791
0.236
21.05
0.830
0.242
VactGaussian' [30]
21,80
0.728
25.20
0.242
0.264
21.01
0.699
0.261
22.64
0.761
0.261
Hierarcby-GG GS [25]
21.52
0.723
0.297
24.64
0.755
0.284
DOGS [10)
0.759
0.765
0.257
21.94
0.740
0.244
24.42
0.804
Cityaassiinn [34]
0.262
0.256
MixG3 (Ours)
25:83
0.261
26.06
0.292
0.267
23:39
0.815
0.219
24.20
0.856
0.220
Otiginal Image
Moga NoRF
CityGarssaan
DOGS
MixGS (Ours)
Ground Truth
Rubble
Buil ding
Rosidance
SchAAt
Qualitative comparison of rendering on the Mill19 [53] and UrbanScene3D [31] datasets. We demonstrate the zoomed-in in detail of the selected areas in red box.
Figure
Qualitative results. Fig. presents the qualitative comparisons of the novel view synthesis results. For MegaNeRF', CityGaussian', and DOGS all visualizations are generated using official pretrained models or results. It can be observed that MixGS outperforms existing methods by producing sharper textures, more accurate illumination, and improved geometric consistency. Specifically, MegaNeRF lacks fine details and exhibit blurry and erroneous structures image rendering. Additionally, MixGS demonstrates significantly better visual quality under challenging lighting conditions, such as the example on the 2nd row Fig. Additional visualization results are provided in the Appendix.
Rendering speed. To further assess the efficiency of our method, we compare the rendering speed (FPS) with other approaches. As shown Table, traditional Nase- methods, such as Mega-NeRF and Switch-NeRF, exhibit significantly slower rendering speed (<0.1 FPS). In contrast, 3DGS-based methods achieve much higher rendering speeds. Benefit from the acceleration provided by the CUDA/C+ implementation and lightweight network design, MixGS achieve real-time speed (2 30 FPS) on all large-scale scenes, even when running on single RTX 3090 GPU, and outperforms or matches methods with more powerful A100 GPUs. To ensure accurate and fair measurements of rendering times per frame, we explicitly synchronize all CUDA streams prior to timing. For more analysis about training time and consumption across methods, please refer to the Appendix.
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Table 2. Rendering speed comparison on the UrbanScene3D [31]
Table 3. Quantitative ablation results on the Rubble dataset.
Mill19 [53] datasets. report the FPS used GPU type.
Mothods
GPU Type
Building
Rubble
Residence
Sci-Art
Model
PSNR1
SSIM
LPIPS.
Mega NeRF [53]
A100 400
<0.1
<0.1
<0.1
<0.1
w/o HE
24.70
0.728
0.324
Switch-NcRF [74]
A100 40G
<0.1
wlo AE
26.24
0.784
0.277
GP-NeRF [63]
A A100 40G
0.42
0.40
0.31
0.34
w/o CT
25.86
0.763
0.307
122]
A100 40G
w/o IT
25.99
0.759
0.311
City Gaussinn (w/ LoD) [34]
A100 40G
37.4
52.6
41.6
64.6
CityGussiin (w/o LoD) [34]
A100 40G
24.3
43.9
32.7
56.1
w/o GV
23.65
0.704
0.360
MixGS (Ours)
3090 24G
33.5
42.5
36.6
53.9
full model
26.66
0.792
0.267
Rostorizar Render
Main Aifferencn-Am
Gouzsions Mixing
Decoted Gaacane
Maod Cruzzaare
1917-1917 SSMEE
PSAR=JAU7 S304-003015
1SSNI-2522 dBl MEM-OUB
Goond Truh:
Figure 4. Visualizations of the Gaussians in the view frustum, decoded Gaussians, and mixed Gaussians, along with their corresponding rendered images. It can be observed that rendering with mixed Gaussians leads to better reconstruction of lighting consistency and fine-graines local details.
4.3 Ablation Study
Key components. Table presents results of ablation experiments designed to assess the individual contributions of key components in the proposed MixGS framework. We remove modify each module and evaluate its impact on reconstruction performance, using the Rubble scene as a example.
First, we examine the effect of multi-resolution hash encoding (w/o HE). Removing this module results in substantial performance drop, with PSNR decreasing by approximately 1.96dB compared the full model. This clearly demonstrates the critical role of hash encoding in representing the 3D positional attributes of Gaussians. Second, we assess the impact of auxiliary Gaussian enhancement (w/o AE). Excluding this component leads to a noticeable decline in performance, primarily due to the absence of camera pose and other Gaussian attribute information. This result highlights the necessity of incorporating auxiliary features to effectively capture the complex variations present in large-scale scenes. We further validate the effectiveness of the multi-stage training strategy. The 3rd and 4th rows of Table correspond to the ablation of the first-stage coarse Gaussian training (w/o CT) and the second-stage implicit feature training (w/o ITT,, respectively. Omitting either stage prevents the optimization process from converging to an optimal solution, indicating that both global structure initialization and local detail refinement we investigate the role of the offset pool (w/o OP), where positional offsets are directly regressed by the decoder rather than learned through dedicated pool. As shown in the 5th row, this leads to slight decrease in reconstruction performance, as direct regression introduces greater uncertainty and makes more challenging capture fine geometric details.
essential for high-quality reconstruction. Next,
Additionally, we conduct an ablation on mixed Gaussian rendering (w/o GV), where only the decoded Gaussians used for rendering, without combining them with the original Gaussians in the view frustum. This configuration results significant degradation of reconstruction quality, as relying solely on implicit features makes it difficult to maintain the global structure of large-scale scenes. To further illustrate the benefits of Gaussian mixing, Fig. visualizes the Gaussians in the view frustum, the decoded Gaussians, and the mixed Gaussians, along with their corresponding rendered images. It is evident that rendering with mixed Gaussians leads to superior reconstruction of lighting consistency and fine-grained local details.
Process in highly textured regions. To ensure that richly textured areas are faithfully reconstructed, MixGS employs two complementary mechanisms.

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CityGausiian
MixGS (Ous)
Ground Truth
Figure S. Qualitative results near scene boundary on the Building scene.
Adaptive Densification: On the one hand, in the coarse training stage, Gaussians are duplicated and split in regions with high-frequency textures and detailed structure (as in 3DGS [22]). As a result, areas with complex details are initially covered by a denser set of coarse Gaussians. This seeding provides a strong prior for the decoded Gaussians to refine fine details.
SffisetDrrvent Refinement: On the other hand, benefiting from the offset pool, the decoded Gaussians will move toward rndostreconttruuted and high-frequency zones during training, instead of locating near the corresponding coarse Gaussians. We show the center location of the coarse and decoded Gaussians in Fig. It can be observed that the decoded Gaussians shift toward more complex areas (e.g., bush and rooftop tiles), further increasing the Gaussian density exactly where it is needed most.
Moreover, MixGS can generate multiple decoded Gaussians per coarse Gaussian by simply modifying the decoder's ' output dimension. We evaluated different ratios K (i.e., N4- = N,) on the Rubble scene in Table 4. It can be observed that higher ratios improve quality (PSNR reaches 27.02 dB at 3:1) but increase memory usage. We use the 1:1 setting for optimal balance between performance and memory efficiency on single 24GB GPU.
Table 4. Quantitative results of different ratio K on the Rubbic scene.
Ratio K
PSNR
SSIM
I.IPS.).
Peak VRAM
26.66
0.792
0.267
20.45 GB
26.76
0.796
0.263
22.91 GB
27.02
0.801
0.255
25.41 GB
Importance of holistic optimization. We include additional visualization results in Fig. to illustrate the differences between holistic optimization and the divide-and-conque strategy. For clarity, the block boundaries are explicitly visualized. Although some post-processing operations [10, 34] with increased overlap are applied to mitigate boundary effects, it can still be observed that the divide-and- conquer approach leads to inconsistent illumination and discontinuities along the block boundaries (red box), as well as obvious black artifacts along the block borders (green box). In contrast, thanks to the advantages of holistic optimization, MixGS effectively avoids such issues.
5 Conclusion
In this paper, we propose MixGS, novel framework for large-scal novel view synthesis. In contrast to mainstream approaches that adopt the divide-and-conquer paradigm, MixGS treats the entire scene as a unified whole and performs holistic optimization. Our method introduces a view-aware represen- tation modeling strategy that integrates camera pose and Gaussian attributes to extract informative local features. By leveraging a multi-hcad decoder and an offset pool, MixGS generates decoded Gaussians that effectively capture fine-grained scene details. The final image rendering is achieved by mixing the Gaussians within the view frustum with the decoded Gaussians, resulting in high-quality scene reconstruction. Extensive experiments MixGS achieves state-of-ther -ftt----rtt-ttttt performance in view synthesis quality, while maintaining efficiency on single GPU. Importantly, MixGS provides fundamentally new perspective for large-scale scene reconstruction, opening up promising directions for future research.
multiple challenging datasets demonstrate that
Acknowledgments. This work is supported by the National Natural Science Foundation of China (No.62325111, 11, U22B2011, 1, 62271354) and the Fundamental Research Funds for the Central Univer- sities (204205kf0063).
10

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A Analysis of 6x Downsampling
Table S. Quantitative results of method compared to previous work on Mill19 dataset [53] and UrbanScene3D dataset [31]. We report metries for PSNR1. SSIMf, and LPIPS on test views. The best and second best are highlighted.
Scenes
Buikding
Rubbic
Residence
Sci-An
SSIM
PSNK
SSIM
LPIPS
PSNR
SSIM
LPIPS.
PSNR
SSIM
LPIPS
DOGS (6x)
22.73
0.759
0.204
25.78
0.768
0.257
21.94
0.740
0.244
24.42
0.804
0.219
MixGS (Ours) (4x)
23.03
0.251
0.261
26.66
0.792
0.267
23.20
0.815
0.219
24.20
0.856
0.220
MivGS (Ours) (6x)
23,82
0,8142
0.197
27.95
0846
0.189
23.96
0.859
0.159
24.36
0.578
0.1648
Consistent with most previous works [53, 34, 30], we primarily downsample all images by a factor of 4, while DOGS [10] employs higher downsampling rate of 6 or more. We observe that increasing the downsampling factor to 6 leads to significantly improved results compared to a factor of 4, as the reconstruction task becomes less challenging due the reduction of high-frequency informa- tion. As shown in Table 5, our method achieves substantially better performance when images are downsampled by a factor 6.
B Evaluation on Additional Datasets
we have evaluated MixGS on the MatrixCity and Campus scenes. The MatrixCity dataset [27] comprises 5,620 training views and 741 test views, while the Campus subset of UrbanScene3D includes 5,850 training images and 21 test images. Both datasets are recognized for their challenging scale and freeueryyyncy details. As shown in Table 6, on both MatrixCity and Campus, MixGS achieves the highest rendering quality across PSNR, SSIM, and LPIPS, demonstrating its robustness across diverse large-scale scenes.
Why LPIPS Is Relatively Higher? From Table 1, it can be observed that MixGS has better PSNR that significant. Our MixGS choice prioritizes holistic
metrics, while the LPIPS metrics consistency (critical for large scenes), instead of trivial LPIPS optimization. Hence, this does not reflect poor detail recovery. Methods like DOGS and CityGaussian use divide-and-conquer, which can create "pixel-periee but lighting inconsistent" local regions (lower LPIPS for small patches) but fail to align textures/lighting across block boundaries (e.g., grassland suddenly shifts in the rows and 2 columns in Fig. 6). Our holistic framework avoids this by enforcing global consistency. which may introduce minor local color adjustments to align large structures, and these adjustments are what LPIPS occasionally penalizes.
Table 6. Additional quantitative results of novel view synthesis the MatrixCity [27] dataset and Campus [53] scene. indicates results obtained without applying decoupled appearance encoding.
Matrixcity
Campus SSIM
Method
PSNR
SSIM
LPIPS
PSNR
LPIPS 1
MegaNeRF [53]
23.42
0.537
0.636
SwitchNeRF [74]
23.62
0.541
0.616
3DGS [22]
23.67
0.735
0.384
18.95
0.630
0.447
GausSiisi [30]
28.33
0.835
0.220
23.82
0.695
0.329
CiyyGassiann [34]
27.32
0,813
0:211
DOGS [10]
28.58
0.847
0.219
24.01
0.681
0.377
Ours
28.63
0.857
0.210
24.28
0.715
0.315
C Computational Overhead
To analysis the trade off between different methods, we compare the computational overhead on both the Mill19 and UrbanScene3D datasets. Table 7, we report the optimization time, GPU memory consumption during evaluation, and the type of GPU used. For Mega-NeRF, Switch-NeRF, VastGaussian, and DOGS, we directly adopt the corresponding data reported in the original DOGS paper, where training was conducted on five RTX 6000 GPUs with 48GB memory each. For 3DGS and our MixGS, we conduct experiments on single RTX 3090 GPU paired with an Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz, and report the corresponding metrics. The software environment
15

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Table Quantitative results of novel view synthesis the Mill19 and UrbanScene3D datasets. We report the training time (hh:mm) and allocated memory (GB). indicates results obtained without applying decoupled appearance encoding.
Building Opt. Time
Rubble
Residence Opt. Time
Sci-Art
Methods
Used GPU
Mem.
Opt. Time
Mem.
Mem.
Ope. Time
Mem.
Mega NeRF [53]
5x RTX6000 48GB
19:49
5.84
30:48
5.88
27:20
5.99
27:39
5.97
Switch-NeRF [74]
6x RTX6000 48GB
5.84
38:30
34:34
3DGS [22]
1x RTX3090 24GB
02:39
4.62
02:24
2.18
02:49
3.23
02:03
1.61
GastGaussian| [30]
5x RTX6000 48GB
03:26
3.07
02:30
2.74
03:12
3.67
02:33
3,54
[10]
RTX6000 48GB
3.39
2.54
3.53
MixGS (Ours)
1x RTX3090 24GB
14:39
3.25
12:07
2.76
17:09
2.83
14:48
2.24
includes PyTorch version 2.0.1 and CUDA version 11.7. Traditional NeRF-based methods, Mega- NeRF and Switch-NeRF, exhibit significantly longer optimization times and require substantial computational resources. In contrast, Gaussian-basse approaches demonstrate considerably lower optimization times and more efficient memory usage. Compared with other methods, MixGS delivers more stable memory consumption across scenes, with memory usage tightly bounded.
Limitations and future work: Currently, due to the single-GPU setup, method requires more training time compared to multi-GPU approaches. As future direction, we plan to adopt the open- source distributed training and accelerate training framework reduce the overall time consumption.
D More Qualitative Results
We provide additional visual comparisons for the Rubble [53], Building [53]. Residence [31], and Sci- Art [31] scenes. Our method consistently reconstructs finer details across all these scenes. Notably, MixGS demonstrates superior capability in reconstructing luminance, as exemplified by the Rubble and Building results shown in Fig. 6. For better visualization, we use red arrow to indicate the differences. This highlights the effectiveness of our holistic optimization framework, which prevents local optimum and loss of global information.
Furthermore, we present depth rendering results in Fig. 7. Depth maps are computed using the default depth rendering technique [23] for 3DGS, without employing any depth regularization or additional supervision. For City CityGausiian [34], we utilize the official checkpoints. As shown in Fig. 7, our method effectively captures fine-grate fine-grained geometric details while avoiding floating artifacts. This demonstrates its strong ability to maintain multi-view consistency and preserve appearance information across large set of images, ultimately enabling large-scale scene reconstruction with highly accurate geometric representation. These results further highlight the potential of our approach for future large-scale mesh reconstruction.
16

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3DGS
City Gaussian
MixGS (Ours)
Ground Truth
Rubble
Rubble
Building
Building
Residence
Residence
Sci-Art
Sci-Art
Figure 6. Qualitative results of ours and other methods in image rendering on Mill-19 [53] and Urban-
scene3D [31] datasets.
17

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3DGS
City Gaussian
MixGS (Ours)
Original Image
Rubble
Rubble
Building
Building
Residence
Residence
Sci-Art
Sci-Art
Figure Qualitative results of ours and other methods depth rendering on Mill-19 [53] and Urbanscene3D [31] datasets.
18

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Reviews:
[{"review_id": "dvoGA9Syp8", "reviewer": "Reviewer_ywp9", "summary": "This paper presents a new paradigm for large-scale scene reconstruction, which combines coarse explicit Gaussians and detailed neural Gaussians, showing improved rendering quality on the selected datasets.", "questions": "1. The authors claimed disconsitnuity of global structure, illumination, and geometry across block boundaries as one main drawbacks of previous works. However, there is no qualitative comparison or quantitative analysis on boundary areas. Indeed, existing works such as VastGaussian and CityGaussianV2 show no clear evidence about texture or geometry discontinuity in boundary. Considering that, this part is to some extent overclaimed.\n2. The datasets used in experiments are limited. Recent works like CityGaussian and DoGS compared at least 5 datasets. The authors should supplement more results on other challenging cases, like the Campus of UrbanScene3D dataset or the MatrixCity dataset.\n3. Excluding the appearance embedding for VastGaussian in the comparison doesn't seem to be fair. The consideration of appearance variations is an advantage and part of the model structure. The authors should include it and make the comparison fairer.", "limitations": "The limitation is briefly discussed in the appendix. But a significant one is ignored. Specifically, the number of decoded Gaussians is the same as that of coarse Gaussians within the frustum. However, for some areas with detailed structure and high-frequency textures, it would be difficult for the model to avoid under-reconstruction with limited Gaussians, restricting its rendering quality and application. The authors should provide more explanation on this potential problem and its solutions.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Quality: There exists some overclaim. And the comparison is not sufficiently fair and enough, weakening the solidity of the paper.\n\nClarity: Good writing, the paragraph and figures are easy to read.\n\nSignificance: The overall pipeline reveals a new paradigm for large-scale GS training.\n\nOriginality: The coarse-to-fine multi-stage training and neural Gaussians are existing techniques.", "was_revised": true, "final_justification": "My main concerns have been resolved. Therefore, I changed my score to the borderline accept.", "rebuttal": "We thank the reviewer for thorough evaluation and constructive feedback. We address each concern below:\n\n***Q1: Inconsistencies at Block Boundaries in Divide‑and‑Conquer Methods***\n\nA1: Existing large‑scale scene reconstruction methods employ a divide‑and‑conquer strategy, partitioning the scene into independent blocks for multi‑GPU training. Since each block is optimized in isolation, multiview constraints do not span block boundaries, which can lead to visible artifact or texture misalignment at those boundaries. Methods such as CityGaussian, CityGaussianV2, and VastGaussian attempt to mitigate these artifacts by **increasing the overlap** between adjacent blocks. The more recent DOGS framework further addresses boundary inconsistencies through a dedicated “consensus step.” As stated in the DOGS paper, below information acknowledges that boundary inconsistencies persist in prior methods.\n> “Our method presents higher‑fidelity rendering results than VastGaussian **near the splitting boundary**, which validates the effectiveness and importance of the consensus step” (DOGS, page.17).\n\nDue to independent optimization of blocks breaks multi-view constraints across boundaries, such post-processing cannot fundamentally resolve the boundary inconsistency.\n\n\nRegarding **qualitative comparison**, due to NeurIPS policy preventing us from including boundary issue visualizations in this rebuttal, please refer to **DOGS’s Figures 8 and 11**, which illustrate these boundary effects.\n\nTo provide a **quantitative comparison**, we follow DOGS’s partitioning strategy and select 300 viewpoints across block boundaries in both the Rubble and Building scenes. We render each viewpoint using the DOGS model and our MixGS method, then compute PSNR, SSIM, and LPIPS for both in below table. Despite DOGS’s consensus refinement, MixGS consistently yields higher quality at block interfaces, effectively eliminating inconsistencies at the source rather than relying on post-processing. \n\n\n\n| Methods |        | Rubble |         |           | Building  |           |\n|:-------:|:------:|:------:|:-------:|:---------:|:---------:|:---------:|\n| Metrics | PSNR ↑ | SSIM ↑ | LPIPS ↓ |  PSNR ↑   |  SSIM ↑   |  LPIPS ↓  |\n|   DOGS  | 27.93  | 0.858  |  0.209  |   25.23   |   0.848   |   0.215   |\n|   Ours  | **29.18**  | **0.865**  |  **0.187**  | **26.40** | **0.859** | **0.201** |\n\n\nCompared with other methods, MixGS optimizes the entire scene as a single entity: all Gaussians are updated under global multi-view constraints, and no block partitioning is performed. This holistic design eliminates boundary inconsistencies at the source, rather than relying on post-processing alignment.\nWe appreciate the reviewer’s suggestion: in the revised manuscript, we will include a clearer discussion of block‑boundary issues and provide illustrative visualizations in the supplementary material.\n\n[1] Dogs: Distributed-oriented gaussian splatting for large-scale 3d reconstruction via gaussian consensus. NeurIPS 2024.\n\n---\n\n***Q2: Evaluation on Additional Datasets***\n\nA2: As suggested, we have evaluated MixGS on the MatrixCity and Campus scenes. The MatrixCity dataset comprises 5,620 training views and 741 test views, while the Campus subset of UrbanScene3D includes 5,850 training images and 21 test images. Both datasets are recognized for their challenging scale and high-frequency details.\n\n\n\n| Method   |              |  Matrixcity       |        |          |     Campus    |        |\n|----------|:--------------:|:---------:|:--------:|:----------:|:---------:|:--------:|\n| Metric   | PSNR  ↑         | SSIM  ↑    | LPIPS ↓ | PSNR  ↑     | SSIM  ↑    | LPIPS ↓ |\n| MegaNeRF | -            | -       | -      | 23.42    | 0.537   | 0.636  |\n| SwitchNeRF | -            | -       | -      | 23.62    | 0.541   | 0.616  |\n| 3DGS     | 23.67        | 0.735   | 0.384  | 18.95    | 0.630   | 0.447  |\n| VastGaussian   | 28.33        | 0.835   | 0.220  | 23.82    | 0.695   | 0.329  |\n| CityGaussian     | 27.32        | 0.813   | 0.211  | -        | -       | -      |\n| DOGS     | 28.58        | 0.847   | 0.219  | 24.01    | 0.681   | 0.377  |\n| Ours     | **28.63**        | **0.857**   | **0.210**  | **24.28**    | **0.715**   | **0.315**  |\n\nOn both MatrixCity and Campus, MixGS achieves the highest rendering quality across PSNR, SSIM, and LPIPS, demonstrating its robustness across diverse large-scale scenes. We will include these quantitative results and corresponding qualitative comparisons in the revised manuscript. Thanks.\n\n---\n\n***Q3: Fair Comparison Setup Regarding Appearance Embedding***\n\nA3: The Appearance Embedding (AE) module used in VastGaussian introduces a lightweight CNN that refines rendered images from the 3D Gaussian pipeline as input and encodes the discrepancy between these renderings and the ground-truth images. This component serves as an image-level post-processing refinement that operates on the rendered output, rather than directly enhancing the underlying 3D Gaussian representation. Therefore, in our main manuscript, we followed the same evaluation protocol as DOGS by disabling the AE module to ensure a fair comparison of the underlying 3D representation and rendering quality.\n\nAs suggested by the reviewer, we also implement and test the AE module for further comparisons. The complete results are shown in the table below:\n\n| Scenes              |             | Building  |           |           | Rubble     |            |           | Residence |           |           | Sci-Art   |           |\n|---------------------|:-----------:|:-----------:|:-----------:|:-----------:|:------------:|:------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| Metrics             |    PSNR↑    | SSIM↑     | LPIPS↓    | PSNR↑     | SSIM↑      | LPIPS↓     | PSNR↑     | SSIM↑     | LPIPS↓    | PSNR↑     | SSIM↑     | LPIPS↓    |\n| Mega-NeRF           |    20.92    | 0.547     | 0.454     | 24.06     | 0.553      | 0.508      | 22.08     | 0.628     | 0.401     | 25.60     | 0.770     | 0.312     |\n| Switch-NeRF         |    21.54    | 0.579     | 0.397     | 24.31     | 0.562      | 0.478      | 22.57     | 0.654     | 0.352     | 26.51     | 0.795     | 0.271     |\n| GP-NeRF             |    21.03    | 0.566     | 0.486     | 24.06     | 0.565      | 0.496      | 22.31     | 0.661     | 0.448     | 25.37     | 0.783     | 0.373     |\n| 3DGS                |    20.46    | 0.720     | 0.305     | 25.47     | 0.777      | 0.277      | 21.44     | 0.791     | 0.236     | 21.05     | 0.830     | 0.242     |\n| VastGaussian        |    21.80    | 0.728     | 0.225     | 25.20     | 0.742      | 0.264      | 21.01     | 0.699     | 0.261     | 22.64     | 0.761     | 0.261     |\n| **VastGaussian+AE** |  **22.34**  | **0.747** | **0.220** | **25.42** | **0.755**  | **0.261**  | **21.84** | **0.711** | **0.251** | **23.04** | **0.797** | **0.249** |\n| Hierarchy-GS        |    21.52    | 0.723     | 0.297     | 24.64     | 0.755      | 0.284      | —         | —         | —         | —         | —         | —         |\n| DOGS                |    22.73    | 0.759     | 0.204     | 25.78     | 0.763      | 0.257      | 21.94     | 0.740     | 0.244     | 24.42     | 0.804     | 0.219     |\n| CityGaussian        |    21.67    | 0.764     | 0.262     | 24.90     | 0.785      | 0.256      | 21.90     | 0.805     | 0.217     | 21.34     | 0.833     | 0.232     |\n| MixGS (Ours)        |    23.03    | 0.771     | 0.261     | 26.66     | 0.792      | 0.267      | 23.39     | 0.815     | 0.219     | 24.20     | 0.856     | 0.220     |\n\nThese results show that even with AE enabled, MixGS maintains superior performance across all metrics, confirming that its advantage stems from the holistic 3D modeling framework rather than the absence of post-processing.\n\n\n---\n\n***Q4: Limited Number of Gaussians in Highly Textured Regions***\n\nA4: To ensure that richly textured areas are faithfully reconstructed, MixGS employs two complementary mechanisms.\n\n**Adaptive Densification**: On the one hand, in the coarse training stage, Gaussians are duplicated and split in regions with high-frequency textures and detailed structure (as in 3DGS [1]). As a result, areas with complex details are initially covered by a denser set of coarse Gaussians. This seeding provides a strong prior for the decoded Gaussians to refine fine details.\n\n**Offset-Driven Refinement**: On the other hand, benefiting from the offset pool, the decoded Gaussians will move toward under-reconstructed and high-frequency zones during training, instead of locating near the corresponding coarse Gaussians. We show the center location of the coarse and decoded Gaussians in Figure 4 of the manuscript. It can be observed that the decoded Gaussians shift toward more complex areas (e.g., bush and rooftop tiles in Fig.4), further increasing the Gaussian density exactly where it is needed most. \n\nMoreover, MixGS can generate multiple decoded Gaussians per coarse Gaussian by simply modifying the decoder's output dimension.\nWe evaluated different ratios K (i.e., $N_{\\Phi} : N_v$) on the Rubble scene:\n\n\n| Ratio K | PSNR ↑ | SSIM ↑ | LPIPS ↓ | Peak VRAM |\n|:---------:|:--------:|:--------:|:---------:|:-----------:|\n| 1       | 26.66  | 0.792  | 0.267   | 20.45 GB  |\n| 2       | 26.76  | 0.796  | 0.263   | 22.91 GB  |\n| 3       | 27.02  | 0.801  | 0.255   | 25.41 GB  |\n\n\nHigher ratios improve quality (PSNR reaches 27.02 dB at 3:1) but increase memory usage. We use the 1:1 setting for optimal balance between performance and memory efficiency on a single 24GB GPU.\n\n\n[1] 3D Gaussian Splatting for Real-Time Radiance Field Rendering. TOG 2023."}, {"review_id": "QlSGhdx8rc", "reviewer": "Reviewer_AjZV", "summary": "This paper proposes a method that enhances the image quality by adding one additional Gaussian splat per each coarse Gaussian splat. To learn the parameter field of the decoded Gaussian splats, a multi-level hash encoding is used. The spatial feature from hash encoding is then combined with the local features, including splat scale, orientation, and camera pose. The decoder finally generates additional Gaussian splats with spatial offsets. The quantitative and qualitative comparison demonstrates the effectiveness of the proposed method.", "questions": "1. For the given view frustum, do we need to expand it to maximize image quality? The expanded frustum includes coarse Gaussians, of which decoded Gaussians enhance the quality near the image boundary.\n\n2.  Does the neural decoder consistently produce a smooth image result when moving the viewpoint?\n\n3. Do you have an idea about how to address the post-processing of coarse Gaussians?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "There are the following strong points.\n\nThe proposed method is promising as the additional Gaussian splats are jointly optimized to enhance the quality. \n\nThe hash-based neural parameter field doesn’t require a large memory size.\n\nThe ablation study thoroughly demonstrates the efficacy of each module.\n\nI found the following weak points.\n\nThe decoded Gaussian splat could be far from a coarse Gaussian splat. This means that coarse Gaussians outside the frustum may be necessary to enhance image quality. This effect would be significant near frustum boundaries.\n\nIf coarse Gaussians are edited after optimization, the decoder part won’t produce the same image enhancement. \n\nThe network may produce unstable reconstructions at novel views, as it is not trained on dense camera views. This is crucial when we synthesize a video with a moving camera.", "was_revised": true, "final_justification": "The rebuttal resolves my concerns. I think generating auxiliary Gaussians by MLP is a novel idea. I am leaning towards the positive for the publication of this paper.", "rebuttal": "We thank the reviewer for thorough evaluation and constructive feedback. We address each concern below:\n\n\n***Q1: Position of Decoded Gaussians***\n\nA1: We agree that decoded Gaussians may shift significantly relative to their coarse counterparts, because our offset pool tends to move primitives toward high‑frequency, texture‑rich regions within the current view frustum (e.g., bush and rooftop tiles panels in Fig.4) to improve local rendering quality.\n\nCrucially, we observe through experiments that the decoded Gaussians remain strictly inside the original view frustum. This behavior emerges naturally during training: \nany decoded Gaussian shifting outside the view frustum would not project onto the current image plane (by definition of the frustum), thus contributing nothing to the rendered pixel values. Consequently, such Gaussians receive **zero loss gradient** during backpropagation, and the network naturally learns to restrict decoded Gaussians to regions that affect the current view. For the current camera pose, coarse Gaussians outside the frustum are irrelevant to rendering, so their exclusion does not sacrifice information needed for the current view.\nIn other words, decoded Gaussians can roam freely within the view frustum to capture scene information, but they will not wander outside the frustum, since such movement would not improve (and indeed would harm) the training objective.\n\n\n---\n\n\n***Q2: Should we expand the view frustum?***\n\nA2: Expanding the view frustum brings more coarse Gaussians into the visible set and thus yields additional decoded Gaussians for rendering the same camera view. In other words, a larger frustum promotes more primitives contributing to the image and can improve quality.\n\nAs suggested by the reviewer, we expand the frustum by scaling the camera’s translation by a factor of 1.05, simulating raising a drone's altitude to capture the scene. Note that during the rendering process, all the camera poses remain unchanged so that the rendered images still correspond to the same ground truth. \nThe table below compares standard MixGS to the expanded view frustum (EV) on four benchmark scenes. The expanded view frustum could consistently improves reconstruction quality for Rubble and Residence scenes, demonstrating the benefit of including more Gaussians in the frustum.\nDue to time constraints, we could not explore this idea more deeply. We appreciate the reviewer’s insightful suggestion, which is interesting for our future research.\n\n| Scenes            |         | Building  |          |         |  Rubble  |          |         |  Residence  |          |         |  Sci-Art  |          |\n|-------------------|:-------:|:---------:|:--------:|:-------:|:--------:|:--------:|:-------:|:-----------:|:--------:|:-------:|:---------:|:--------:|\n| Metrics           |  PSNR↑  |   SSIM↑   |  LPIPS↓  |  PSNR↑  |  SSIM↑   |  LPIPS↓  |  PSNR↑  |    SSIM↑    |  LPIPS↓  |  PSNR↑  |   SSIM↑   |  LPIPS↓  |\n| Mega-NeRF         |  20.92  |   0.547   |  0.454   |  24.06  |  0.553   |  0.508   |  22.08  |    0.628    |  0.401   |  25.60  |   0.770   |  0.312   |\n| Switch-NeRF       |  21.54  |   0.579   |  0.397   |  24.31  |  0.562   |  0.478   |  22.57  |    0.654    |  0.352   |  26.51  |   0.795   |  0.271   |\n| GP-NeRF           |  21.03  |   0.566   |  0.486   |  24.06  |  0.565   |  0.496   |  22.31  |    0.661    |  0.448   |  25.37  |   0.783   |  0.373   |\n| 3DGS              |  20.46  |   0.720   |  0.305   |  25.47  |  0.777   |  0.277   |  21.44  |    0.791    |  0.236   |  21.05  |   0.830   |  0.242   |\n| VastGaussian      |  21.80  |   0.728   |  0.225   |  25.20  |  0.742   |  0.264   |  21.01  |    0.699    |  0.261   |  22.64  |   0.761   |  0.261   |\n| Hierarchy-GS      |  21.52  |   0.723   |  0.297   |  24.64  |  0.755   |  0.284   |    —    |      —      |    —     |    —    |     —     |    —     |\n| DOGS              |  22.73  |   0.759   |  0.204   |  25.78  |  0.763   |  0.257   |  21.94  |    0.740    |  0.244   |  24.42  |   0.804   |  0.219   |\n| CityGaussian      |  21.67  |   0.764   |  0.262   |  24.90  |  0.785   |  0.256   |  21.90  |    0.805    |  0.217   |  21.34  |   0.833   |  0.232   |\n| MixGS       |  23.03  |   0.771   |  0.261   |  26.66  |  0.792   |  0.267   |  23.39  |    0.815    |  0.219   |  24.20  |   0.856   |  0.220   |\n| MixGS + EV |  22.93  |   0.783   |  0.250   |  26.76  |  0.796   |  0.263   |  23.47  |    0.825    |  0.215   |  24.24  |   0.851   |  0.225   |\n\n\n---\n\n\n***Q3: Post-Processing of Gaussians***\n\nA3: If the coarse Gaussians are edited, such as being repositioned or having their appearance changed, our MixGS framework can adapt efficiently. In cases of moderate changes, the decoder can simply relearn the offsets without requiring retraining from scratch. Since the decoder is conditioned on the updated coarse Gaussians, it can reassign detail representations to match the new configuration.\n\nFor more substantial edits, we can freeze the coarse Gaussians and fine-tune only the decoder in the affected regions. This localized adaptation enables MixGS to flexibly handle post-processed scenes without globally re-optimizing the full representation, making it particularly suitable for downstream editing tasks.\n\n\n---\n\n\n***Q4: Does the method produce smooth results with a moving camera?***\n\nA4: On the one hand, all the quantitative results reported in our paper are evaluated on the official test set, which contains viewpoints unseen during training. Despite this, our method consistently achieves high rendering quality, demonstrating its generalization ability.\n\nOn the other hand, due to NeurIPS policy, we are unable to provide an anonymous link to show qualitative videos rendered under a densely sampled, unseen camera trajectory. \n\nTo further validate the smoothness of our method under continuous camera motion, we conduct experiments on the large‐scale MatrixCity dataset [1]. MatrixCity comprises 5,620 training images and **741 test images**, with the test set designed to provide densely sampled, consecutive viewpoints. We evaluated our approach alongside several baselines and report the results in the table below. It can be observed that our method achieves the highest results across all metrics, demonstrating its robustness in producing smooth, high‐quality renderings as the camera moves continuously.\nWe appreciate the reviewer’s comments and will integrate video results into our revision.\n\n\n| Method   |              |  Matrixcity       |        |\n|----------|:--------------:|:---------:|:--------:|\n| Metric   | PSNR  ↑         | SSIM  ↑    | LPIPS ↓ |\n| MegaNeRF | -            | -       | -      |\n| SwitchNeRF | -            | -       | -      |\n| 3DGS     | 23.67        | 0.735   | 0.384  |\n| VastGaussian   | 28.33        | 0.835   | 0.220  |\n| CityGaussian     | 27.32        | 0.813   | 0.211  |\n| DOGS     | 28.58        | 0.847   | 0.219  | \n| Ours     | **28.63**        | **0.857**   | **0.210**  |\n\n[1] Matrixcity: A large-scale city dataset for city-scale neural rendering and beyond. ICCV 2023."}, {"review_id": "p8WDxpAjE8", "reviewer": "Reviewer_KRUu", "summary": "This paper proposes MixGS, which aims to reconstruct large-scale scenes via 3D Gaussian Splatting. MixGS utilizes both vanilla gaussian representation and the neural network-based gaussian representation to represent the large-scale scenes. The vanilla gaussias reconstruct the coarse information, and the neural gaussians capture fine-grained details, enabling reconstruction of large-scale scenes with less gaussian primitives.", "questions": "My major concerns are the low training time efficiency and the confusing effects of the method. I will raise my score if the authors provide a convincing analysis of these two weaknesses in the rebuttal.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The proposed method MixGS achieves state-of-the-art rendering quality on Mill19 and UrbanScene3D datasets, especially for the PSNR and SSIM metrics.\n2. The idea to incorporate neural gaussians to capture details with vanilla gaussians is interesting. The neural gaussians are helpful to model view-dependent colors, which may be hard for vanilla gaussians due to the constraints of sh-degree.\n3. The paper is well-written and easy to follow..\n\nWeaknesses:\n1. The method has a significant drawback in training time, as shown in Table 2 of the supplementary material. MixGS needs about 15 hours to optimize a large-scale scene, while the previous 3d gaussian splatting-based methods need about 3 hours. Although the authors discuss this drawback in the limitation section in the supplementary material, I still think the training time is the weakness because training efficiency is a crucial issue for large-scale scene reconstruction .\n2. Although the authors claim that using neural gaussians is to capture fine-grained details, there is a lack of deeper analysis about the actual effects. The method uses the  View Frustum to select the gaussians to pass through the neural network and introduces camera poses as auxiliary information. This implementation introduces lots of view-dependent information to render the scenes. In a large-scale scene, the same scene often has very different lighting from different views. Therefore, it is confusing whether the improvement comes from a reasonable modeling of the scene or from introducing a representation of view-specific information. From Table 1, MixGS has better PSNR metrics, while the LPIPS metrics are not that significant. Modeling of specific view information can lead to an improvement in PSNR, while lpipis can well measure high-frequency information. And from Figure 3, I also find MixGS has an advantage in rendering view-dependent lightning effects, while the details improvement is not significant.", "was_revised": true, "final_justification": "The proposed method achieves good global consistency of using 3DGS for large-scale rendering. While the high LPIPS metrics make me uncertain whether the LPIPS gap is solely due to global consistency, or also reflects differences in local detail reconstruction. Overall, I appreciate the benefits of the proposed method for global consistency and rate the boardline accept.", "rebuttal": "We thank the reviewer for thorough evaluation and constructive feedback. We address each concern below:\n\n\n***Q1: Training Time Efficiency***\n\nWe acknowledge the reviewer’s concern about training time. The longer training duration in our original results is primarily due to hardware limitations: while other methods (as shown in Table 2 of the supplementary material) were trained using five high-end GPUs (5 × RTX 6000 48GB), our experiments were conducted on a single RTX 3090 24GB. This significant difference in hardware configuration directly impacts training efficiency.\n\nTo provide a more fair comparison, we integrate MixGS into the open-source distributed training framework [1] and retrain our model using 5 × RTX 3090 GPUs. The training time comparison is summarized below:\n\n| Methods               | Used GPU        |  Building  |  Rubble  | Residence  |  Sci-Art  |\n|-----------------------|-----------------|:----------:|:--------:|:----------:|:---------:|\n| Mega-NeRF         | 5× RTX6000 48GB |   19:49    |  30:48   |   27:20    |   27:39   |\n| Switch-NeRF     | 5× RTX6000 48GB |   24:46    |  38:30   |   35:11    |   34:34   |\n| 3DGS             | 1× RTX3090 24GB |   02:39    |  02:24   |   02:49    |   02:03   |\n| VastGaussian†     | 5× RTX6000 48GB |   03:26    |  02:30   |   03:12    |   02:33   |\n| DOGS             | 5× RTX6000 48GB |   03:51    |  02:25   |   04:33    |   04:23   |\n| MixGS (Ours)         | 1× RTX3090 24GB |   14:39    |  12:07   |   17:09    |   14:48   |\n| MixGS (Ours)         | 5× RTX3090 24GB |   03:48    |  03:02   |   04:28    |   04:13   |\n\n\nWith five GPUs, training time of MixGS drops to a range comparable with or better than existing methods.\n\nFurthermore, we confirm that distributed training preserves rendering quality. Below are the quantitative results comparing the original single-GPU model and our multi-GPU version. \nThese results demonstrate that MixGS can scale effectively under distributed GPU training while preserving high rendering quality. We appreciate the reviewer’s suggestion and will include this discussion and additional results in the revised version.\n\n\n| Scenes          |         |  Building  |          |         |  Rubble  |          |         |  Residence  |          |         |  Sci-Art  |          |\n|-----------------|:-------:|:----------:|:--------:|:-------:|:--------:|:--------:|:-------:|:-----------:|:--------:|:-------:|:---------:|:--------:|\n| Metrics         |  PSNR↑  |   SSIM↑    |  LPIPS↓  |  PSNR↑  |  SSIM↑   |  LPIPS↓  |  PSNR↑  |    SSIM↑    |  LPIPS↓  |  PSNR↑  |   SSIM↑   |  LPIPS↓  |\n| Mega-NeRF       |  20.92  |   0.547    |  0.454   |  24.06  |  0.553   |  0.508   |  22.08  |    0.628    |  0.401   |  25.60  |   0.770   |  0.312   |\n| Switch-NeRF     |  21.54  |   0.579    |  0.397   |  24.31  |  0.562   |  0.478   |  22.57  |    0.654    |  0.352   |  26.51  |   0.795   |  0.271   |\n| GP-NeRF         |  21.03  |   0.566    |  0.486   |  24.06  |  0.565   |  0.496   |  22.31  |    0.661    |  0.448   |  25.37  |   0.783   |  0.373   |\n| 3DGS            |  20.46  |   0.720    |  0.305   |  25.47  |  0.777   |  0.277   |  21.44  |    0.791    |  0.236   |  21.05  |   0.830   |  0.242   |\n| VastGaussian    |  21.80  |   0.728    |  0.225   |  25.20  |  0.742   |  0.264   |  21.01  |    0.699    |  0.261   |  22.64  |   0.761   |  0.261   |\n| Hierarchy-GS    |  21.52  |   0.723    |  0.297   |  24.64  |  0.755   |  0.284   |    —    |      —      |    —     |    —    |     —     |    —     |\n| DOGS            |  22.73  |   0.759    |  0.204   |  25.78  |  0.763   |  0.257   |  21.94  |    0.740    |  0.244   |  24.42  |   0.804   |  0.219   |\n| CityGaussian    |  21.67  |   0.764    |  0.262   |  24.90  |  0.785   |  0.256   |  21.90  |    0.805    |  0.217   |  21.34  |   0.833   |  0.232   |\n| MixGS (x1 GPUs) |  23.03  |   0.771    |  0.261   |  26.66  |  0.792   |  0.267   |  23.39  |    0.815    |  0.219   |  24.20  |   0.856   |  0.220   |\n| MixGS (x5 GPUs) |  22.95  |   0.781    |  0.253   |  26.63  |  0.807   |  0.253   |  23.38  |    0.824    |  0.216   |  24.13  |   0.853   |  0.222   |\n\n\n[1] gsplat: An open-source library for Gaussian splatting. JMLR 2025. \n\n\n\n---\n\n\n***Q2: Deep analysis of the proposed method***\n\n**1. Differences Between PSNR and LPIPS**\n\nWe appreciate the reviewer’s attention to metric consistency. However, it is critical to note that PSNR and LPIPS measure fundamentally distinct aspects of reconstruction quality, and their divergence does not inherently indicate a failure in detail recovery:\n\n* **PSNR** quantifies pixel-level intensity/color error (MSE-based). A high PSNR confirms that models accurately match the ground truth in terms of low-level color/brightness fidelity, which is a **prerequisite** for detail recovery, as fine textures rely on precise pixel values.\n\n* **LPIPS** measures perceptual similarity using pre-trained CNN features (e.g., VGG), which are sensitive to high-level structural cues (e.g., edge continuity, semantic consistency) but may overpenalize certain benign discrepancies (e.g., subtle lighting variations in large scenes that humans barely notice).\n\nIn large-scale scenes, LPIPS can be elevated by factors unrelated to \"lack of details\": for example, global illumination consistency (a strength of our holistic framework) may introduce minor local color adjustments to align lighting across the scene, which LPIPS sometimes flags despite preserving fine textures.\n\n**2. Fine Detail Recover of MixGS**\n\nWe directly demonstrate the capability of MixGS for detail recovery:\n\n2.1 **Qualitative Proof:** According to the policy for NeurIPS 2025, we are unable to include additional links or figures. Please refer to Row 3 and Row 5 of Figure 1 in the supplementary materials. These clearly demonstrate that our method reconstructs richer details in the areas indicated by arrows, particularly for the rooftop tiles and blue building, despite exhibiting marginally lower LPIPS scores in certain scenes.\n\n2.2 **Quantitative Proof:** In experiments where we removed auxiliary view-dependent information from the Rubble scene, i.e., camera poses and rotation/scaling parameters of Gaussian splats, the results presented in the table below demonstrate that reducing view-dependent information consistently degrades PSNR while simultaneously increasing LPIPS. Consequently, it would be inaccurate to simplistically conclude that incorporating more view-dependent information inherently deteriorates LPIPS performance.\n\n| Methods                                       | PSNR↑ | SSIM↑ | LPIPS↓ |\n|-----------------------------------------------|-------|-------|--------|\n| Ours w/o auxiliary view-dependent information | 26.24 | 0.784 | 0.277  |\n| Ours (full model)                             | 26.66 | 0.792 | 0.267  |\n\nAdditionally, we evaluate MixGS on the MatrixCity and Campus scenes. The MatrixCity dataset comprises 5,620 training views and 741 test views, while the Campus subset of UrbanScene3D includes 5,850 training images and 21 test images. Both datasets are recognized for their challenging scale and high-frequency details.\nAs shown in the table below, on both MatrixCity and Campus, MixGS achieves the highest rendering quality across PSNR, SSIM, and LPIPS. We will include these quantitative results and corresponding qualitative comparisons in the revised manuscript.\n\n\n| Method   |             |  Matrixcity  |             |             |   Campus   |            |\n|----------|:-----------:|:------------:|:-----------:|:-----------:|:----------:|:----------:|\n| Metric   |   PSNR ↑    |    SSIM ↑    |   LPIPS ↓   |   PSNR ↑    |   SSIM ↑   |  LPIPS ↓   |\n| MegaNeRF |      -      |      -       |      -      |    23.42    |   0.537    |   0.636    |\n| SwitchNeRF |      -      |      -       |      -      |    23.62    |   0.541    |   0.616    |\n| 3DGS     |    23.67    |    0.735     |    0.384    |    18.95    |   0.630    |   0.447    |\n| VastGaussian   |    28.33    |    0.835     |    0.220    |    23.82    |   0.695    |   0.329    |\n| CityGaussian     |    27.32    |    0.813     |    0.211    |      -      |     -      |     -      |\n| DOGS     |    28.58    |    0.847     |    0.219    |    24.01    |   0.681    |   0.377    |\n| Ours     |  **28.63**  |  **0.857**   |  **0.210**  |  **24.28**  | **0.715**  | **0.315**  |\n\n\n**3. Why LPIPS Is Relatively Higher?**\n\nOur MixGS choice to prioritizes holistic consistency (critical for large scenes), instead of trivial LPIPS optimization. Hence, this does not reflect poor detail recovery.\nMethods like DOGS and CityGaussian use divide-and-conquer, which can create \"pixel-perfect but lighting inconsistent\" local regions (lower LPIPS for small patches) but fail to align textures/lighting across block boundaries (e.g., grassland suddenly shifts in the 4 rows and 2 columns in Fig. 1 of supplementary).\nOur holistic framework avoids this by enforcing global consistency, which may introduce minor local color adjustments to align large structures, and these adjustments are what LPIPS occasionally penalizes.\n\n**4. Conclusion**\n\nThe observed phenomenon of higher PSNR with slightly elevated LPIPS reflects a trade-off in our framework to maintain global consistency in large-scale scenes, rather than indicating any failure in detail recovery. Furthermore, our MixGS approach achieves reasonable scene modeling through an explicit coarse Gaussian training phase combined with implicit decoded Gaussians, while simultaneously incorporating view-specific representation information. This cooperative combination ultimately produces superior rendering results. We appreciate the reviewer's comments and will include additional discussion to eliminate confusing description, along with more representative comparative visualizations in our revision."}, {"review_id": "xBY6EBYpFG", "reviewer": "Reviewer_buD9", "summary": "This work proposes a large-scale scene reconstruction framework, MixGS, based on Gaussian Splatting. Specifically, this work introduces a Mixed Gaussian representation, combined by Coarse 3D Gaussians from inital points and Shifted Gaussians from view-aware positional encoding. The Mixed Gaussian representation is optimized and further rendered for final results. Experimental results on several datasets show the effectiveness of the proposed method.", "questions": "1. It would be better to provide some certain cases to the problem.\n2. How can areas with extremely sparse initialization (e.g., due to textureless input) be effectively processed? It would be better to provide some examples?\n3. Why does the proposed MixGS show consistently lower results in the human perception metric (LPIPS), as shown in Table 1?\n4. Is it feasible to compare all the methods on the same platform?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Paper Strengths\n\nThis work focuses on two limitations of previous algorithms, and experimental results on several datasets show the effectiveness of the proposed method.\n\n\n\n\n### Major Weaknesses\n\n\n\nIntroduction  \n1. The limitations of pervious algorithms. The paper highlights two limitations of previous algorithms (Complex Parameter Tuning and Loss of Global Information), but it does not present specific examples. It would be better to provide some certain cases to the problem.\n\nMethod\n1. Coarse 3D Gaussians Training. As illustrated in this section, Guassians are initialized from sparse sfm points. According to line 199 ('Note that the number of decoded Gaussians are the same with Gaussians in the view frustum'), decoded Gaussians are correlated to the initial sets. How can areas with extremely sparse initialization (e.g., due to textureless input) be effectively processed?\n\n2. View-Aware Representation Modeling. The proposed process of learning view-aware Gaussians is quite similar to Scaffold-GS [1]. It would be better to discuss the difference between the proposed method and Scaffold-GS. \n\n\n[1] Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering\n\n\nExperiments\n1. Quantitative Results. Why does the proposed MixGS show consistently lower results in the human perception metric (LPIPS), as shown in Table 1?\n2. Rendering Speed. Is it feasible to compare all the methods on the same platform?\n\n\n\n\n\n\n### Minor Weaknesses\n\ntypo\n1. line 121: Define -> Definition", "was_revised": true, "final_justification": "After reading the comments from other reviewers and the authors' rebuttal, the reviewer maintains the rating as borderline accept.", "rebuttal": "We thank the reviewer for thorough evaluation and constructive feedback. We address each concern below:\n\n***Q1: Specific Cases of the Problems***\n\n**1. Complex Parameter Tuning**\n\nPrior divide-and-conquer approaches rely heavily on carefully crafted view assignment strategies for each individual block, which introduces considerable tuning complexity.\nFor example, VastGaussian requires setting a visibility threshold to determine whether a particular camera view should contribute to a block, while CityGaussian introduces an intersection threshold for selection. Beyond that, parameters must also be decided for how many blocks to divide the scene into and define the splitting intervals along each axis. These hyperparameters vary across scenes and typically require manual adjustment or grid search, significantly increasing implementation burden and making reproduction more challenging.\n\n**2. Loss of Global Information**\n\nExisting large‑scale scene reconstruction methods employ a divide‑and‑conquer strategy, partitioning the scene into independent blocks for multi‑GPU training. Since each block is optimized in isolation, multiview constraints do not span block boundaries, which can lead to visible artifact or texture misalignment at those boundaries. Methods such as CityGaussian, CityGaussianV2, and VastGaussian attempt to mitigate these artifacts by **increasing the overlap** between adjacent blocks. The more recent DOGS framework further addresses boundary inconsistencies through a dedicated “consensus step.” As stated in the DOGS paper, below information acknowledges that boundary inconsistencies persist in prior methods.\n> “Our method presents higher‑fidelity rendering results than VastGaussian **near the splitting boundary**, which validates the effectiveness and importance of the consensus step” (DOGS, page.17).\n\nDue to independent optimization of blocks breaks multi-view constraints across boundaries, such post-processing cannot fundamentally resolve the boundary inconsistency.\n\n\nRegarding **qualitative comparison**, due to NeurIPS policy preventing us from including boundary issue visualizations in this rebuttal, please refer to **DOGS’s Figures 8 and 11**, which illustrate these boundary effects.\n\nTo provide a **quantitative comparison**, we follow DOGS’s partitioning strategy and select 300 viewpoints across block boundaries in both the Rubble and Building scenes. We render each viewpoint using the DOGS model and our MixGS method, then compute PSNR, SSIM, and LPIPS for both in below table. Despite DOGS’s consensus refinement, MixGS consistently yields higher quality at block interfaces, effectively eliminating inconsistencies at the source rather than relying on post-processing. \n\n\n\n| Methods |        | Rubble |         |           | Building  |           |\n|:-------:|:------:|:------:|:-------:|:---------:|:---------:|:---------:|\n| Metrics | PSNR ↑ | SSIM ↑ | LPIPS ↓ |  PSNR ↑   |  SSIM ↑   |  LPIPS ↓  |\n|   DOGS  | 27.93  | 0.858  |  0.209  |   25.23   |   0.848   |   0.215   |\n|   Ours  | **29.18**  | **0.865**  |  **0.187**  | **26.40** | **0.859** | **0.201** |\n\n\nCompared with other methods, MixGS optimizes the entire scene as a single entity: all Gaussians are updated under global multi-view constraints, and no block partitioning is performed. This holistic design eliminates boundary inconsistencies at the source, rather than relying on post-processing alignment.\nWe appreciate the reviewer’s suggestion: in the revised manuscript, we will include a clearer discussion of block‑boundary issues and provide illustrative visualizations in the supplementary material.\n\n[1] Dogs: Distributed-oriented gaussian splatting for large-scale 3d reconstruction via gaussian consensus. NeurIPS 2024.\n\n\n---\n\n\n***Q2: Processing Areas with Extremely Sparse Initialization***\n\nTo ensure that richly textured areas are faithfully reconstructed, MixGS employs two complementary mechanisms.\n\n**Adaptive Densification**: On the one hand, in the coarse training stage, Gaussians are duplicated and split in regions with high-frequency textures and detailed structure (as in 3DGS [1]). As a result, areas with complex details are initially covered by a denser set of coarse Gaussians. This seeding provides a strong prior for the decoded Gaussians to refine fine details.\n\n**Offset-Driven Refinement**: On the other hand, benefiting from the offset pool, the decoded Gaussians will move toward under-reconstructed and high-frequency zones during training, instead of locating near the corresponding coarse Gaussians. We show the center location of the coarse and decoded Gaussians in Figure 4 of the manuscript. It can be observed that the decoded Gaussians shift toward more complex areas (e.g., bush and rooftop tiles in Fig.4), further increasing the Gaussian density exactly where it is needed most. \n\nMoreover, MixGS can generate multiple decoded Gaussians per coarse Gaussian by simply modifying the decoder's output dimension.\nWe evaluated different ratios K (i.e., $N_{\\Phi} : N_v$) on the Rubble scene:\n\n\n| Ratio K | PSNR↑ | SSIM↑ | LPIPS↓ | Peak VRAM |\n|:-------:|:-----:|:-----:|:------:|:---------:|\n|    1    | 26.66 | 0.792 | 0.267  | 20.45 GB  |\n|    2    | 26.76 | 0.796 | 0.263  | 22.91 GB  |\n|    3    | 27.02 | 0.801 | 0.255  | 25.41 GB  |\n\n\nHigher ratios improve quality (PSNR reaches 27.02 dB at 3:1) but increase memory usage. We use the 1:1 setting for optimal balance between performance and memory efficiency on a single 24GB GPU.\n\n\n[1] 3D Gaussian Splatting for Real-Time Radiance Field Rendering. TOG 2023.\n\n\n\n---\n\n\n***Q3: Different from Scaffold-GS***\n\nScaffold‑GS builds a hierarchical, region‑aware representation by introducing fixed anchor points and dynamically spawning Gaussians around them based on view coverage and feature saliency. In contrast, MixGS explores spatial structure through a unified multi‑resolution hash encoding of Gaussian centers, enabling implicit feature learning at multiple scales.\n\nMoreover, MixGS encodes not only each Gaussian’s position but also its auxiliary attributes, such as rotation, scale, and camera pose, into a high‑level feature vector. These rich, view‑aware features are then decoded by a lightweight MLP to generate fine‑detail Gaussians, whose positions are refined via an offset pool. Finally, our novel mixing mechanism combines the original “coarse” Gaussians with the decoded “fine” Gaussians into a single set for rendering, ensuring both global consistency and local fidelity across an entire large‑scale scene.\n\nIn summary, while Scaffold‑GS adaptively assigns Gaussians per region and view, MixGS holistically optimizes the full scene representation, jointly learning coarse structure and fine details, without any block partitioning or anchor placement. We will add this comparison in the revision to clarify these conceptual and technical distinctions.\n\n---\n\n***Q4: Why LPIPS Is Relatively Higher?***\n\n\nOur MixGS choice to prioritizes holistic consistency (critical for large scenes), instead of trivial LPIPS optimization. Hence, this does not reflect poor detail recovery.\nMethods like DOGS and CityGaussian use divide-and-conquer, which can create \"pixel-perfect but lighting inconsistent\" local regions (lower LPIPS for small patches) but fail to align textures/lighting across block boundaries (e.g., grassland suddenly shifts in the 4 rows and 2 columns in Fig. 1 of supplementary).\nOur holistic framework avoids this by enforcing global consistency, which may introduce minor local color adjustments to align large structures, and these adjustments are what LPIPS occasionally penalizes.\n\n\nAdditionally, we evaluate MixGS on the MatrixCity and Campus scenes. Both datasets are recognized for their challenging scale and high-frequency details.\nAs shown in the table below, MixGS achieves the highest rendering quality across PSNR, SSIM, and LPIPS. We will include these quantitative results and corresponding qualitative comparisons in the revised manuscript.\n\n\n| Method   |           | Matrixcity |           |           |  Campus   |          |\n|----------|:---------:|:----------:|:---------:|:---------:|:---------:|:--------:|\n| Metric   |  PSNR ↑   |   SSIM ↑   |  LPIPS ↓  |  PSNR ↑   |  SSIM ↑   |  LPIPS ↓ |\n| MegaNeRF |   -       |     -      |     -     |   23.42   |  0.537    |  0.636   |\n| SwitchNeRF |     -     |     -      |     -     |  23.62    |   0.541   |   0.616  |\n| 3DGS     |   23.67   |   0.735    |   0.384   |   18.95   |   0.630   |   0.447  |\n| VastGaussian   |   28.33   |   0.835    |   0.220   |   23.82   |   0.695   |   0.329  |\n| CityGaussian     |   27.32   |  0.813     |   0.211   |     -     |     -     |     -    |\n| DOGS     |   28.58   |   0.847    |  0.219    |   24.01   |   0.681   |   0.377  |\n| Ours     | **28.63** | **0.857**  | **0.210** | **24.28** | **0.715** | **0.315** |\n\n\n\n---\n\n\n***Q5: Rendering Speed Comparison on the Same Platform***\n\nDue to hardware constraints, we are unable to benchmark MixGS on A100 GPUs. Instead, we measure all methods on an NVIDIA RTX 3090 to ensure a fair comparison. The results are shown in the Table below. We will include this direct comparison in the revised manuscript for greater clarity.\n\n| Methods               | GPU Type | Building | Rubble | Residence | Sci-Art |\n|-----------------------|----------|:--------:|:------:|:---------:|:------:|\n| 3DGS                 | RTX 3090 |   62.8   |  79.4  |   64.5    |  98.7  |\n| CityGaussian (w/ LoD) | RTX 3090 |   45.4   |  59.8  |   49.5    |  69.4  |\n| CityGaussian (w/o LoD)| RTX 3090 |   30.7   |  48.1  |   36.3    | 60.6   |\n| MixGS (Ours)         | RTX 3090 |  33.5    |  42.5  |   36.6    |  53.9  |\n\n\n---\n\n\n***Q6: Typo Error***\n\nThank you for your careful review. We will perform a comprehensive proofread of the manuscript and correct all grammatical and spelling mistakes in the revised version."}]

Author rebuttal:

Meta-review: After rebuttal, all reviewers lean to accept the paper. The AC thereby recommends acceptance of the paper.  The authors are required to include the new materials and results during the rebuttal into the final version, e.g., the discussion on the LPIPS metric.
Decision: Accept (poster)