Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting

• URL: http://arxiv.org/abs/2505.23280v1
• Date: Thu, 29 May 2025 09:25:36 GMT
• Title: Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting
• Authors: Chuandong Liu, Huijiao Wang, Lei Yu, Gui-Song Xia,
• Abstract summary: MixGS is a novel holistic optimization framework for large-scale 3D scene reconstruction.<n>Experiments on large-scale scenes demonstrate that MixGS achieves state-of-the-art rendering quality and competitive speed.
• Score: 30.31985970421813
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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. The code will be released at https://github.com/azhuantou/MixGS.

Related papers

• A LoD of Gaussians: Unified Training and Rendering for Ultra-Large Scale Reconstruction with External Memory [8.972911362220803]
  We introduce A LoD of Gaussians, a framework for training and rendering ultra-large-scale scenes on a single consumer-grade GPU.<n>A hybrid data structure combining Gaussian hierarchies with Sequential Point Trees enables efficient, view-dependent LoD selection.<n>A lightweight caching and view scheduling system exploits temporal coherence to support real-time streaming and rendering.
  arXiv  Detail & Related papers  (2025-07-01T18:12:43Z)
• HoliGS: Holistic Gaussian Splatting for Embodied View Synthesis [59.25751939710903]
  We propose a novel deformable Gaussian splatting framework that addresses embodied view synthesis from long monocular RGB videos.<n>Our method leverages invertible Gaussian Splatting deformation networks to reconstruct large-scale, dynamic environments accurately.<n>Results highlight a practical and scalable solution for EVS in real-world scenarios.
  arXiv  Detail & Related papers  (2025-06-24T03:54:40Z)
• BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via Adaptive Block-Based Gaussian Splatting [23.190361456894816]
  BlockGaussian is a novel framework incorporating a content-aware scene partition strategy and visibility-aware block optimization.<n>Our approach achieves state-of-the-art performance in both reconstruction efficiency and rendering quality, with a 5x speedup in optimization and an average PSNR improvement of 1.21 dB on multiple benchmarks.<n> Notably, BlockGaussian significantly reduces computational requirements, enabling large-scale scene reconstruction on a single 24GB VRAM device.
  arXiv  Detail & Related papers  (2025-04-12T02:05:55Z)
• HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction [46.269350101349715]
  HiSplat is a novel framework for generalizable 3D Gaussian Splatting. It generates hierarchical 3D Gaussians via a coarse-to-fine strategy. It significantly enhances reconstruction quality and cross-dataset generalization.
  arXiv  Detail & Related papers  (2024-10-08T17:59:32Z)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios.<n>We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo [54.00987996368157]
  We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) MVSGaussian achieves real-time rendering with better synthesis quality for each scene.
  arXiv  Detail & Related papers  (2024-05-20T17:59:30Z)
• CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians [64.6687065215713]
  CityGaussian employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and rendering. Our approach attains state-of-theart rendering quality, enabling consistent real-time rendering of largescale scenes across vastly different scales.
  arXiv  Detail & Related papers  (2024-04-01T14:24:40Z)
• VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction [59.40711222096875]
  We present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets.
  arXiv  Detail & Related papers  (2024-02-27T11:40:50Z)
• FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting [58.41056963451056]
  We propose a few-shot view synthesis framework based on 3D Gaussian Splatting. This framework enables real-time and photo-realistic view synthesis with as few as three training views. FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency across diverse datasets.
  arXiv  Detail & Related papers  (2023-12-01T09:30:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.