GSFusion:Globally Optimized LiDAR-Inertial-Visual Mapping for Gaussian Splatting

• URL: http://arxiv.org/abs/2507.23273v1
• Date: Thu, 31 Jul 2025 06:15:51 GMT
• Title: GSFusion:Globally Optimized LiDAR-Inertial-Visual Mapping for Gaussian Splatting
• Authors: Jaeseok Park, Chanoh Park, Minsu Kim, Soohwan Kim,
• Abstract summary: We propose GSFusion, an online LiDAR-Inertial-Visual mapping system that ensures high-precision map consistency through a surfel-to-surfel constraint in the global pose-graph optimization.<n> Experiments on public and our datasets demonstrate our system outperforms existing 3DGS SLAM systems in terms of rendering quality and map-building efficiency.
• Score: 20.665306154754564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized photorealistic mapping, conventional approaches based on camera sensor, even RGB-D, suffer from fundamental limitations such as high computational load, failure in environments with poor texture or illumination, and short operational ranges. LiDAR emerges as a robust alternative, but its integration with 3DGS introduces new challenges, such as the need for exceptional global alignment for photorealistic quality and prolonged optimization times caused by sparse data. To address these challenges, we propose GSFusion, an online LiDAR-Inertial-Visual mapping system that ensures high-precision map consistency through a surfel-to-surfel constraint in the global pose-graph optimization. To handle sparse data, our system employs a pixel-aware Gaussian initialization strategy for efficient representation and a bounded sigmoid constraint to prevent uncontrolled Gaussian growth. Experiments on public and our datasets demonstrate our system outperforms existing 3DGS SLAM systems in terms of rendering quality and map-building efficiency.

Related papers

• GDGS: 3D Gaussian Splatting Via Geometry-Guided Initialization And Dynamic Density Control [6.91367883100748]
  Gaussian Splatting is an alternative for rendering realistic images while supporting real-time performance.<n>We propose a method to enhance 3D Gaussian Splatting (3DGS)citeKerbl2023, addressing challenges in initialization, optimization, and density control.<n>Our method demonstrates comparable or superior results to state-of-the-art methods, rendering high-fidelity images in real time.
  arXiv  Detail & Related papers  (2025-07-01T01:29:31Z)
• TVG-SLAM: Robust Gaussian Splatting SLAM with Tri-view Geometric Constraints [22.121665995381324]
  TVG-SLAM is a robust RGB-only 3DGS SLAM system that leverages a novel tri-view geometry paradigm to ensure consistent tracking and high-quality mapping.<n>Our method improves tracking robustness, reducing the average Absolute Trajectory Error (ATE) by 69.0% while achieving state-of-the-art rendering quality.
  arXiv  Detail & Related papers  (2025-06-29T12:31:05Z)
• Intern-GS: Vision Model Guided Sparse-View 3D Gaussian Splatting [95.61137026932062]
  Intern-GS is a novel approach to enhance the process of sparse-view Gaussian splatting.<n>We show that Intern-GS achieves state-of-the-art rendering quality across diverse datasets.
  arXiv  Detail & Related papers  (2025-05-27T05:17:49Z)
• Steepest Descent Density Control for Compact 3D Gaussian Splatting [72.54055499344052]
  3D Gaussian Splatting (3DGS) has emerged as a powerful real-time, high-resolution novel view.<n>We propose a theoretical framework that demystifies and improves density control in 3DGS.<n>We introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud.
  arXiv  Detail & Related papers  (2025-05-08T18:41:38Z)
• Diffusion-Guided Gaussian Splatting for Large-Scale Unconstrained 3D Reconstruction and Novel View Synthesis [22.767866875051013]
  We propose GS-Diff, a novel 3DGS framework guided by a multi-view diffusion model to address limitations of current methods.<n>By generating pseudo-observations conditioned on multi-view inputs, our method transforms under-constrained 3D reconstruction problems into well-posed ones.<n> Experiments on four benchmarks demonstrate that GS-Diff consistently outperforms state-of-the-art baselines by significant margins.
  arXiv  Detail & Related papers  (2025-04-02T17:59:46Z)
• GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping [22.432252084121274]
  LiDAR-Inertial-Visual (LIV) sensor configuration has demonstrated superior performance in localization and dense mapping.<n>We propose a novel real-time Gaussian-based simultaneous localization and mapping (SLAM) system.<n>The framework achieves real-time performance while maintaining robust multi-sensor fusion capabilities.
  arXiv  Detail & Related papers  (2025-01-15T09:04:56Z)
• Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels [51.08794269211701]
  We introduce 3D Linear Splatting (3DLS), which replaces Gaussian kernels with linear kernels to achieve sharper and more precise results.<n>3DLS demonstrates state-of-the-art fidelity and accuracy, along with a 30% FPS improvement over baseline 3DGS.
  arXiv  Detail & Related papers  (2024-11-19T11:59:54Z)
• GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering [69.67264955234494]
  GeoSplatting is a novel approach that augments 3DGS with explicit geometry guidance for precise light transport modeling.<n>By differentiably constructing a surface-grounded 3DGS from an optimizable mesh, our approach leverages well-defined mesh normals and the opaque mesh surface.<n>This enhancement ensures precise material decomposition while preserving the efficiency and high-quality rendering capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-31T17:57:07Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.<n>Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting [50.808933338389686]
  We present LiDAR-GS, a real-time, high-fidelity re-simulation of LiDAR scans in public urban road scenes.<n>The method achieves state-of-the-art results in both rendering frame rate and quality on publically available large scene datasets.
  arXiv  Detail & Related papers  (2024-10-07T15:07:56Z)
• HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes [24.227745405760697]
  We propose a hybrid optimization method named HO-Gaussian, which combines a grid-based volume with the 3DGS pipeline. Results on widely used autonomous driving datasets demonstrate that HO-Gaussian achieves photo-realistic rendering in real-time on multi-camera urban datasets.
  arXiv  Detail & Related papers  (2024-03-29T07:58:21Z)
• Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting [55.71424195454963]
  Spec-Gaussian is an approach that utilizes an anisotropic spherical Gaussian appearance field instead of spherical harmonics. Our experimental results demonstrate that our method surpasses existing approaches in terms of rendering quality. This improvement extends the applicability of 3D GS to handle intricate scenarios with specular and anisotropic surfaces.
  arXiv  Detail & Related papers  (2024-02-24T17:22:15Z)

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.