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 76–100 of 271 papers

TitleStatusHype
RenderNet: A deep convolutional network for differentiable rendering from 3D shapesCode0
Physically Disentangled RepresentationsCode0
NOVUM: Neural Object Volumes for Robust Object ClassificationCode0
ROSA: Reconstructing Object Shape and Appearance Textures by Adaptive Detail TransferCode0
Differentiable Monte Carlo Ray Tracing through Edge SamplingCode0
Light Sampling Field and BRDF Representation for Physically-based Neural RenderingCode0
Fine-Grained Multi-View Hand Reconstruction Using Inverse RenderingCode0
IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor ScenesCode0
Face Inverse Rendering via Hierarchical DecouplingCode0
Deep Single-Image Portrait RelightingCode0
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single ImageCode0
Learning to Rasterize DifferentiablyCode0
Neural Lumigraph RenderingCode0
Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency Conditions—0
Environment Maps Editing using Inverse Rendering and Adversarial Implicit Functions—0
End-to-end 3D shape inverse rendering of different classes of objects from a single input image—0
Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation—0
A Theory of Topological Derivatives for Inverse Rendering of Geometry—0
Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparency—0
Efficient multi-view training for 3D Gaussian Splatting—0
Deep Learning compatible Differentiable X-ray Projections for Inverse Rendering—0
Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method—0
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering—0
A Simple Approach to Differentiable Rendering of SDFs—0
Eclipse: Disambiguating Illumination and Materials using Unintended Shadows—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