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

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Showing 51–100 of 271 papers

TitleStatusHype
Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparency—0
RelitLRM: Generative Relightable Radiance for Large Reconstruction Models—0
GI-GS: Global Illumination Decomposition on Gaussian Splatting for Inverse Rendering—0
Flash-Splat: 3D Reflection Removal with Flash Cues and Gaussian Splats—0
RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering—0
MaterialFusion: Enhancing Inverse Rendering with Material Diffusion Priors—0
Colorful Diffuse Intrinsic Image Decomposition in the WildCode3
Phys3DGS: Physically-based 3D Gaussian Splatting for Inverse Rendering—0
Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering—0
A General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse RenderingCode1
DiffCSG: Differentiable CSG via Rasterization—0
MERLiN: Single-Shot Material Estimation and Relighting for Photometric Stereo—0
G3FA: Geometry-guided GAN for Face Animation—0
Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering—0
Progressive Radiance Distillation for Inverse Rendering with Gaussian Splatting—0
Many-Worlds Inverse Rendering—0
PIR: Photometric Inverse Rendering with Shading Cues Modeling and Surface Reflectance RegularizationCode1
MAIR++: Improving Multi-view Attention Inverse Rendering with Implicit Lighting RepresentationCode1
PBIR-NIE: Glossy Object Capture under Non-Distant Lighting—0
Dynamic Scene Understanding through Object-Centric Voxelization and Neural RenderingCode1
UniVoxel: Fast Inverse Rendering by Unified Voxelization of Scene RepresentationCode0
Surfel-based Gaussian Inverse Rendering for Fast and Relightable Dynamic Human Reconstruction from Monocular Video—0
SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization—0
Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation—0
Multi-times Monte Carlo Rendering for Inter-reflection Reconstruction—0
Fine-Grained Multi-View Hand Reconstruction Using Inverse RenderingCode0
Blind Inversion using Latent Diffusion Priors—0
Intrinsic PAPR for Point-level 3D Scene Albedo and Shading Editing—0
Uncertainty for SVBRDF Acquisition using Frequency AnalysisCode1
Inverse Rendering using Multi-Bounce Path Tracing and Reservoir Sampling—0
IllumiNeRF: 3D Relighting Without Inverse Rendering—0
3D Reconstruction with Fast Dipole Sums—0
Don't Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive Media—0
GS-ROR^2: Bidirectional-guided 3DGS and SDF for Reflective Object Relighting and Reconstruction—0
A Simple Approach to Differentiable Rendering of SDFs—0
RGBX: Image decomposition and synthesis using material- and lighting-aware diffusion models—0
ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-view Images—0
Unveiling the Ambiguity in Neural Inverse Rendering: A Parameter Compensation Analysis—0
Inverse Neural Rendering for Explainable Multi-Object Tracking—0
IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination—0
PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations—0
Spin-UP: Spin Light for Natural Light Uncalibrated Photometric StereoCode1
Dr.Hair: Reconstructing Scalp-Connected Hair Strands without Pre-training via Differentiable Rendering of Line Segments—0
Inverse Rendering of Glossy Objects via the Neural Plenoptic Function and Radiance Fields—0
GaNI: Global and Near Field Illumination Aware Neural Inverse Rendering—0
SIR: Multi-view Inverse Rendering with Decomposable Shadow for Indoor Scenes—0
NeRF as a Non-Distant Environment Emitter in Physics-based Inverse Rendering—0
IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images—0
SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild—0
Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object RelightingCode2
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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