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 151–160 of 271 papers

TitleStatusHype
Robust Category-Level 3D Pose Estimation from Synthetic Data—0
Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior—0
Scattering Parameters and Surface Normals from Homogeneous Translucent Materials using Photometric Stereo—0
ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering—0
ZS-SRT: An Efficient Zero-Shot Super-Resolution Training Method for Neural Radiance Fields—0
3D Reconstruction with Fast Dipole Sums—0
A Bayesian Inference Framework for Procedural Material Parameter Estimation—0
Accelerating Inverse Rendering By Using a GPU and Reuse of Light Paths—0
Acquisition of Spatially-Varying Reflectance and Surface Normals via Polarized Reflectance Fields—0
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering—0
Show:102550
← PrevPage 16 of 28Next →

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