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 26–50 of 271 papers

TitleStatusHype
ROSA: Reconstructing Object Shape and Appearance Textures by Adaptive Detail TransferCode0
Materialist: Physically Based Editing Using Single-Image Inverse RenderingCode1
Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models—0
LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fields—0
BG-Triangle: Bezier Gaussian Triangle for 3D Vectorization and Rendering—0
Shading Meets Motion: Self-supervised Indoor 3D Reconstruction Via Simultaneous Shape-from-Shading and Structure-from-Motion—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
PBR-NeRF: Inverse Rendering with Physics-Based Neural FieldsCode1
MaterialPicker: Multi-Modal Material Generation with Diffusion Transformers—0
Differentiable Inverse Rendering with Interpretable Basis BRDFs—0
NeISF++: Neural Incident Stokes Field for Polarized Inverse Rendering of Conductors and Dielectrics—0
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering—0
GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering—0
Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency Conditions—0
All-frequency Full-body Human Image RelightingCode0
URAvatar: Universal Relightable Gaussian Codec Avatars—0
GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering—0
Practical and Accurate Reconstruction of an Illuminant's Spectral Power Distribution for Inverse Rendering Pipelines—0
Environment Maps Editing using Inverse Rendering and Adversarial Implicit Functions—0
GlossyGS: Inverse Rendering of Glossy Objects with 3D Gaussian Splatting—0
Triplet: Triangle Patchlet for Mesh-Based Inverse Rendering and Scene Parameters ApproximationCode1
DEL: Discrete Element Learner for Learning 3D Particle Dynamics with Neural Rendering—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