Multi-times Monte Carlo Rendering for Inter-reflection Reconstruction
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Abstract (arXiv)
Inverse rendering methods have achieved remarkable performance in reconstructing high-fidelity 3D objects with disentangled geometries, materials, and environmental light. However, they still face huge challenges in reflective surface reconstruction. Although recent methods model the light trace to learn specularity, the ignorance of indirect illumination makes it hard to handle inter-reflections among multiple smooth objects. In this work, we propose Ref-MC2 that introduces the multi-time Monte Carlo sampling which comprehensively computes the environmental illumination and meanwhile considers the reflective light from object surfaces. To address the computation challenge as the times of Monte Carlo sampling grow, we propose a specularity-adaptive sampling strategy, significantly reducing the computational complexity. Besides the computational resource, higher geometry accuracy is also required because geometric errors accumulate multiple times. Therefore, we further introduce a reflection-aware surface model to initialize the geometry and refine it during inverse rendering. We construct a challenging dataset containing scenes with multiple objects and inter-reflections. Experiments show that our method outperforms other inverse rendering methods on various object groups. We also show downstream applications, e.g., relighting and material editing, to illustrate the disentanglement ability of our method.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 58 references that are in the map. First posted on arXiv 8 Jul 2024.
Builds on
36 of 58 references.
References · 36
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
- 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
- 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
- 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
- 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,194
- 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,061
- 2021Volume Rendering of Neural Implicit Surfacescited by 990
- 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
- 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 857
- 2023Scaffold-GS: Structured 3D Gaussians for View-Adaptive Renderingcited by 684
- 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 661
24 more
- 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
- 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 569
- 2021Extracting Triangular 3D Models, Materials, and Lighting From Imagescited by 363
- 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 332
- 2023GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfacescited by 249
- 2023BakedSDF: Meshing Neural SDFs for Real-Time View Synthesiscited by 220
- 2023MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenescited by 215
- 2021Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decompositioncited by 204
- 2022Modeling Indirect Illumination for Inverse Renderingcited by 167
- 2023TensoIR: Tensorial Inverse Renderingcited by 166
- 2022Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoisingcited by 163
- 2023NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Imagescited by 163
- 2023Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fieldscited by 142
- 2023Flexible Isosurface Extraction for Gradient-Based Mesh Optimizationcited by 138
- 2021NeRFReN: Neural Radiance Fields with Reflectionscited by 121
- 2023Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflectioncited by 68
- 2023NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functionscited by 65
- 2023ENVIDR: Implicit Differentiable Renderer with Neural Environment Lightingcited by 63
- 2024Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesiscited by 41
- 2023NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illuminationcited by 38
- 2023Neural Microfacet Fields for Inverse Renderingcited by 37
- 2023NeMF: Inverse Volume Rendering with Neural Microflake Fieldcited by 34
- 2023UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflectionscited by 29
- 2023VMesh: Hybrid Volume-Mesh Representation for Efficient View Synthesiscited by 25
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