SOTAVerified

Inverse Rendering

Inverse Rendering is the task of recovering the properties of a scene, such as shape, material, and lighting, from an image or a video. The goal of inverse rendering is to determine the properties of a scene given an observation of it, and to generate new images or videos based on these properties.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 271 papers

TitleStatusHype
A Level Set Theory for Neural Implicit Evolution under Explicit FlowsCode1
Modeling Indirect Illumination for Inverse RenderingCode1
Spelunking the Deep: Guaranteed Queries on General Neural Implicit Surfaces via Range AnalysisCode1
NeAT: Neural Adaptive TomographyCode1
PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor ImagesCode1
Advances in Neural RenderingCode1
NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown IlluminationCode1
Outdoor inverse rendering from a single image using multiview self-supervisionCode1
NeRD: Neural Reflectance Decomposition from Image CollectionsCode1
Building 3D Morphable Models from a Single ScanCode1
Invertible Neural BRDF for Object Inverse RenderingCode1
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion ModelCode1
A Morphable Face Albedo ModelCode1
Learning Inverse Rendering of Faces from Real-world VideosCode1
Superaccurate Camera Calibration via Inverse RenderingCode1
Self-Supervised Linear Motion DeblurringCode1
InverseRenderNet: Learning single image inverse renderingCode1
HiNeuS: High-fidelity Neural Surface Mitigating Low-texture and Reflective Ambiguity—0
TextureSplat: Per-Primitive Texture Mapping for Reflective Gaussian SplattingCode0
Efficient multi-view training for 3D Gaussian Splatting—0
Neural Inverse Rendering from Propagating Light—0
LightLab: Controlling Light Sources in Images with Diffusion Models—0
TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians—0
RGS-DR: Reflective Gaussian Surfels with Deferred Rendering for Shiny Objects—0
Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset—0
3D Gaussian Inverse Rendering with Approximated Global Illumination—0
Learning 3D-Gaussian Simulators from RGB Videos—0
Locally Orderless Images for Optimization in Differentiable Rendering—0
BG-Triangle: Bézier Gaussian Triangle for 3D Vectorization and Rendering—0
Shadow Art Kanji: Inverse Rendering ApplicationCode0
Channel-wise Noise Scheduled Diffusion for Inverse Rendering in Indoor Scenes—0
GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling—0
SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation—0
Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior—0
GlossGau: Efficient Inverse Rendering for Glossy Surface with Anisotropic Spherical Gaussian—0
Generative Detail Enhancement for Physically Based Materials—0
OMG: Opacity Matters in Material Modeling with Gaussian Splatting—0
NPSim: Nighttime Photorealistic Simulation From Daytime Images With Monocular Inverse Rendering and Ray Tracing—0
Multi-view 3D surface reconstruction from SAR images by inverse rendering—0
DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models—0
ROSA: Reconstructing Object Shape and Appearance Textures by Adaptive Detail TransferCode0
LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fields—0
Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models—0
Shading Meets Motion: Self-supervised Indoor 3D Reconstruction Via Simultaneous Shape-from-Shading and Structure-from-Motion—0
BG-Triangle: Bezier Gaussian Triangle for 3D Vectorization and Rendering—0
IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing—0
Uni-Renderer: Unifying Rendering and Inverse Rendering Via Dual Stream Diffusion—0
GenLit: Reformulating Single-Image Relighting as Video Generation—0
Acquisition of Spatially-Varying Reflectance and Surface Normals via Polarized Reflectance Fields—0
MaterialPicker: Multi-Modal Material Generation with Diffusion Transformers—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Neural-PBIRHDR-PSNR26.01—Unverified
2NVDiffRecMCHDR-PSNR24.43—Unverified
3InvRenderHDR-PSNR23.76—Unverified
4NeRFactorHDR-PSNR23.54—Unverified
5NeRDHDR-PSNR23.29—Unverified
6NVDiffRecHDR-PSNR22.91—Unverified
7PhySGHDR-PSNR21.81—Unverified