Title: Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting
Venue: NeurIPS 2025
Accepted: yes
Keywords: Gaussian Splatting

Large-Scale Scene

3D Reconstruction

Novel View Synthesis
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nMy 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nThere 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nQuality: 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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}