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 101–125 of 271 papers

TitleStatusHype
Intrinsic PAPR for Point-level 3D Scene Albedo and Shading Editing—0
InverseFaceNet: Deep Monocular Inverse Face Rendering—0
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images—0
Inverse Neural Rendering for Explainable Multi-Object Tracking—0
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
HiNeuS: High-fidelity Neural Surface Mitigating Low-texture and Reflective Ambiguity—0
Differentiable Surface Rendering via Non-Differentiable Sampling—0
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions—0
Inverse Transport Networks—0
Hardware Acceleration of Neural Graphics—0
CLA-NeRF: Category-Level Articulated Neural Radiance Field—0
Acquisition of Spatially-Varying Reflectance and Surface Normals via Polarized Reflectance Fields—0
IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images—0
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images—0
Joint Learning of Portrait Intrinsic Decomposition and Relighting—0
Joint Sampling and Optimisation for Inverse Rendering—0
Learning 3D-Gaussian Simulators from RGB Videos—0
GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering—0
GS-ROR^2: Bidirectional-guided 3DGS and SDF for Reflective Object Relighting and Reconstruction—0
Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting—0
Differentiable Rendering of Neural SDFs through Reparameterization—0
Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition—0
Environment Maps Editing using Inverse Rendering and Adversarial Implicit Functions—0
GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling—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