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

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Showing 51–75 of 271 papers

TitleStatusHype
A Level Set Theory for Neural Implicit Evolution under Explicit FlowsCode1
Modeling Indirect Illumination for Inverse RenderingCode1
Spelunking the Deep: Guaranteed Queries on General Neural Implicit Surfaces via Range AnalysisCode1
NeAT: Neural Adaptive TomographyCode1
PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor ImagesCode1
Advances in Neural RenderingCode1
NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown IlluminationCode1
Outdoor inverse rendering from a single image using multiview self-supervisionCode1
NeRD: Neural Reflectance Decomposition from Image CollectionsCode1
Building 3D Morphable Models from a Single ScanCode1
Invertible Neural BRDF for Object Inverse RenderingCode1
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion ModelCode1
A Morphable Face Albedo ModelCode1
Learning Inverse Rendering of Faces from Real-world VideosCode1
Superaccurate Camera Calibration via Inverse RenderingCode1
Self-Supervised Linear Motion DeblurringCode1
InverseRenderNet: Learning single image inverse renderingCode1
HiNeuS: High-fidelity Neural Surface Mitigating Low-texture and Reflective Ambiguity—0
TextureSplat: Per-Primitive Texture Mapping for Reflective Gaussian SplattingCode0
Efficient multi-view training for 3D Gaussian Splatting—0
Neural Inverse Rendering from Propagating Light—0
LightLab: Controlling Light Sources in Images with Diffusion Models—0
TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians—0
RGS-DR: Reflective Gaussian Surfels with Deferred Rendering for Shiny Objects—0
Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset—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