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 201–225 of 271 papers

TitleStatusHype
Deep Face Feature for Face Alignment—0
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering—0
Deep Learning compatible Differentiable X-ray Projections for Inverse Rendering—0
Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation—0
DeepShaRM: Multi-View Shape and Reflectance Map Recovery Under Unknown Lighting—0
Deep Structure for end-to-end inverse rendering—0
Deep Uncalibrated Photometric Stereo via Inter-Intra Image Feature Fusion—0
DEL: Discrete Element Learner for Learning 3D Particle Dynamics with Neural Rendering—0
DiffCSG: Differentiable CSG via Rasterization—0
Differentiable Inverse Rendering with Interpretable Basis BRDFs—0
Differentiable Neural Radiosity—0
Differentiable Point-based Inverse Rendering—0
Differentiable Rendering of Neural SDFs through Reparameterization—0
Differentiable Surface Rendering via Non-Differentiable Sampling—0
Diffusion Reflectance Map: Single-Image Stochastic Inverse Rendering of Illumination and Reflectance—0
Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models—0
DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models—0
Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset—0
Physics-based Indirect Illumination for Inverse Rendering—0
Dressi: A Hardware-Agnostic Differentiable Renderer with Reactive Shader Packing and Soft Rasterization—0
Dr.Hair: Reconstructing Scalp-Connected Hair Strands without Pre-training via Differentiable Rendering of Line Segments—0
Eclipse: Disambiguating Illumination and Materials using Unintended Shadows—0
Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method—0
Efficient multi-view training for 3D Gaussian Splatting—0
Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparency—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