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 176–200 of 271 papers

TitleStatusHype
Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects—0
Robust Category-Level 3D Pose Estimation from Synthetic Data—0
Eclipse: Disambiguating Illumination and Materials using Unintended Shadows—0
NOVUM: Neural Object Volumes for Robust Object ClassificationCode0
Inverse Global Illumination using a Neural Radiometric Prior—0
ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering—0
Neural-PBIR Reconstruction of Shape, Material, and Illumination—0
Pointersect: Neural Rendering with Cloud-Ray Intersection—0
Light Sampling Field and BRDF Representation for Physically-based Neural RenderingCode0
Inferring Fluid Dynamics via Inverse Rendering—0
Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes—0
Neural Microfacet Fields for Inverse Rendering—0
Weakly-supervised Single-view Image Relighting—0
MELON: NeRF with Unposed Images in SO(3)—0
Hardware Acceleration of Neural Graphics—0
Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition—0
Optimization-Based Eye Tracking using Deflectometric Information—0
Makeup Extraction of 3D Representation via Illumination-Aware Image Decomposition—0
MEGANE: Morphable Eyeglass and Avatar Network—0
Face Inverse Rendering via Hierarchical DecouplingCode0
ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting—0
Neural Fields Meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes—0
Polarimetric Multi-View Inverse Rendering—0
Physics-based Indirect Illumination for Inverse Rendering—0
SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume 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