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 151–200 of 271 papers

TitleStatusHype
DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse RenderingCode1
Weakly-supervised Single-view Image Relighting—0
WildLight: In-the-wild Inverse Rendering with a FlashlightCode1
Efficient Meshy Neural Fields for Animatable Human AvatarsCode1
MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting EstimationCode1
Learning a 3D Morphable Face Reflectance Model from Low-cost DataCode1
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
NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights, and Materials of Real ObjectCode1
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
Plateau-reduced Differentiable Path Tracing—0
Learning to Rasterize DifferentiablyCode0
Multi-view Inverse Rendering for Large-scale Real-world Indoor ScenesCode1
Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing—0
IBL-NeRF: Image-Based Lighting Formulation of Neural Radiance FieldsCode1
Random Weight Factorization Improves the Training of Continuous Neural Representations—0
IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View SynthesisCode1
A General Scattering Phase Function for Inverse Rendering—0
Polarimetric Inverse Rendering for Transparent Shapes ReconstructionCode1
PRIF: Primary Ray-based Implicit Function—0
Deep Uncalibrated Photometric Stereo via Inter-Intra Image Feature Fusion—0
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo—0
Sobolev Training for Implicit Neural Representations with Approximated Image DerivativesCode1
Self-calibrating Photometric Stereo by Neural Inverse RenderingCode1
Sparse Ellipsometry: Portable Acquisition of Polarimetric SVBRDF and Shape with Unstructured Flash PhotographyCode1
DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated ImagesCode0
GAN2X: Non-Lambertian Inverse Rendering of Image GANs—0
IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor ScenesCode0
TileGen: Tileable, Controllable Material Generation and Capture—0
Differentiable Rendering of Neural SDFs through Reparameterization—0
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and DenoisingCode2
Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior—0
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collectionsCode1
Multiview Textured Mesh Recovery by Differentiable RenderingCode1
Physically-Based Editing of Indoor Scene Lighting from a Single Image—0
A Level Set Theory for Neural Implicit Evolution under Explicit FlowsCode1
Modeling Indirect Illumination for Inverse RenderingCode1
Physically Disentangled RepresentationsCode0
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images—0
Dressi: A Hardware-Agnostic Differentiable Renderer with Reactive Shader Packing and Soft Rasterization—0
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions—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