mlsanityData to 14 Oct 2026

GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering

arXiv 2410.24204 ↗First posted 31 Oct 2024cs.CVcited by 22

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Abstract (arXiv)

Recent 3D Gaussian Splatting (3DGS) representations have demonstrated remarkable performance in novel view synthesis; further, material-lighting disentanglement on 3DGS warrants relighting capabilities and its adaptability to broader applications. While the general approach to the latter operation lies in integrating differentiable physically-based rendering (PBR) techniques to jointly recover BRDF materials and environment lighting, achieving a precise disentanglement remains an inherently difficult task due to the challenge of accurately modeling light transport. Existing approaches typically approximate Gaussian points' normals, which constitute an implicit geometric constraint. However, they usually suffer from inaccuracies in normal estimation that subsequently degrade light transport, resulting in noisy material decomposition and flawed relighting results. To address this, we propose GeoSplatting, a novel approach that augments 3DGS with explicit geometry guidance for precise light transport modeling. By differentiably constructing a surface-grounded 3DGS from an optimizable mesh, our approach leverages well-defined mesh normals and the opaque mesh surface, and additionally facilitates the use of mesh-based ray tracing techniques for efficient, occlusion-aware light transport calculations. This enhancement ensures precise material decomposition while preserving the efficiency and high-quality rendering capabilities of 3DGS. Comprehensive evaluations across diverse datasets demonstrate the effectiveness of GeoSplatting, highlighting its superior efficiency and state-of-the-art inverse rendering performance. The project page can be found at https://pku-vcl-geometry.github.io/GeoSplatting/.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 38 of its 62 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

38 of 62 references.

References · 38

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  3. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
  4. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  5. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,076
  6. 2021Volume Rendering of Neural Implicit Surfacescited by 996
  7. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
  8. 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 616
  9. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 575
  10. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 460
  11. 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 449
  12. 2020Modular Primitives for High-Performance Differentiable Renderingcited by 441
26 more
  1. 2021Extracting Triangular 3D Models, Materials, and Lighting From Imagescited by 366
  2. 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 335
  3. 2024PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstructioncited by 285
  4. 2023GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfacescited by 252
  5. 2024Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenescited by 232
  6. 2023GS-IR: 3D Gaussian Splatting for Inverse Renderingcited by 224
  7. 2019Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderercited by 221
  8. 2021Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decompositioncited by 205
  9. 2022Modeling Indirect Illumination for Inverse Renderingcited by 168
  10. 2023TensoIR: Tensorial Inverse Renderingcited by 168
  11. 2022Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoisingcited by 163
  12. 2023NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Imagescited by 163
  13. 2024High-quality Surface Reconstruction using Gaussian Surfelscited by 161
  14. 2023Flexible Isosurface Extraction for Gradient-Based Mesh Optimizationcited by 139
  15. 20243D Gaussian Ray Tracing: Fast Tracing of Particle Scenescited by 139
  16. 2023GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorizationcited by 87
  17. 20243DGSR: Implicit Surface Reconstruction with 3D Gaussian Splattingcited by 79
  18. 2024GaMeS: Mesh-Based Adapting and Modification of Gaussian Splattingcited by 70
  19. 2023Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflectioncited by 69
  20. 2023ENVIDR: Implicit Differentiable Renderer with Neural Environment Lightingcited by 65
  21. 2021DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderercited by 52
  22. 2023Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmarkcited by 51
  23. 2024GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstructioncited by 31
  24. 2023SIRe-IR: Inverse Rendering for BRDF Reconstruction with Shadow and Illumination Removal in High-Illuminance Scenescited by 19
  25. 2024MeshGS: Adaptive Mesh-Aligned Gaussian Splatting for High-Quality Renderingcited by 19
  26. 2024Direct and Adaptable Mesh-Gaussian Scene Reconstruction from Multi-View Imagescited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 14.

22 papers.

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  1. 2025MaterialRefGS: Reflective Gaussian Splatting with Multi-view Consistent Material Inferencecited by 23
  2. 2026LuxRemix: Lighting Decomposition and Remixing for Indoor Scenescited by 9
  3. 2025MVInverse: Feed-forward Multi-view Inverse Rendering in Secondscited by 6
  4. 2025Reflections Unlock: Geometry-Aware Reflection Disentanglement in 3D Gaussian Splatting for Photorealistic Scenes Renderingcited by 4
  5. 2025UniGS: Unified Geometry-Aware Gaussian Splatting for Multimodal Renderingcited by 2
  6. 2025Training-Free Multi-View Extension of IC-Light for Textual Position-Aware Scene Relightingcited by 2
  7. 2026Radiometrically Consistent Gaussian Surfels for Inverse Renderingcited by 2
  8. 2026Benchmarking PhD-Level Coding in 3D Geometric Computer Visioncited by 2
  9. 2026RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splattingcited by 2
  10. 2026SGS-Intrinsic: Semantic-Invariant Gaussian Splatting for Sparse-View Indoor Inverse Renderingcited by 1
  11. 2026Heat Kernel Textures: the Geodesic Gaussians That Do Not Splatcited by 1
  12. 2025Reprojection-Guided 3D Gaussian Splatting Diffusion for Weakly Supervised Single-Image Normal Estimationcited by 0
10 more
  1. 2025GS-2M: Material-aware Gaussian Splatting for High-fidelity Mesh Reconstructioncited by 0
  2. 2025COREA: Coupled Relightable 3D Gaussians and SDFs for Efficient Normal Alignmentcited by 0
  3. 2026Geo-ID: Test-Time Geometric Consensus for Cross-View Consistent Intrinsicscited by 0
  4. 2026R-PGA: Robust Physical Adversarial Camouflage Generation via Relightable 3D Gaussian Splattingcited by 0
  5. 2026Relightable Gaussian Splatting for Virtual Production Using Image-Based Illuminationcited by 0
  6. 2026TRON: Tracing Rays to Orchestrate a Neural Renderer for 3D Gaussian Reconstructionscited by 0
  7. 2026From Sparse and Imperfect 2D Anchors to Consistent 3D Gaussian Street Scenes: Support-Aware Appearancecited by 0
  8. 2026AEGIR: Modeling Area Emitters for Indoor Inverse Rendering using Gaussian Splattingcited by 0
  9. 2026Manifold-GS: Certified Hybrid Assets via Varifold-Conservative Gaussian Splattingcited by 0
  10. 2026ExMesh++: From Multi-View Images to Relightable UV-PBR Mesh Assets via Topology-Adaptive Reconstruction and Decompositioncited by 0