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Inverse Rendering
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
TextureSplat: Per-Primitive Texture Mapping for Reflective Gaussian Splatting
Efficient multi-view training for 3D Gaussian Splatting
Neural Inverse Rendering from Propagating Light
LightLab: Controlling Light Sources in Images with Diffusion Models
TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians
RGS-DR: Reflective Gaussian Surfels with Deferred Rendering for Shiny Objects
Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset
SVG-IR: Spatially-Varying Gaussian Splatting for Inverse Rendering
3D Gaussian Inverse Rendering with Approximated Global Illumination
Learning 3D-Gaussian Simulators from RGB Videos
Locally Orderless Images for Optimization in Differentiable Rendering
TensoFlow: Tensorial Flow-based Sampler for Inverse Rendering
BG-Triangle: Bézier Gaussian Triangle for 3D Vectorization and Rendering
Shadow Art Kanji: Inverse Rendering Application
Channel-wise Noise Scheduled Diffusion for Inverse Rendering in Indoor Scenes
GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling
SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation
Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior
Generative Detail Enhancement for Physically Based Materials
GlossGau: Efficient Inverse Rendering for Glossy Surface with Anisotropic Spherical Gaussian
OMG: Opacity Matters in Material Modeling with Gaussian Splatting
NPSim: Nighttime Photorealistic Simulation From Daytime Images With Monocular Inverse Rendering and Ray Tracing
Multi-view 3D surface reconstruction from SAR images by inverse rendering
DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models
ROSA: Reconstructing Object Shape and Appearance Textures by Adaptive Detail Transfer
Materialist: Physically Based Editing Using Single-Image Inverse Rendering
IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing
Uni-Renderer: Unifying Rendering and Inverse Rendering Via Dual Stream Diffusion
GenLit: Reformulating Single-Image Relighting as Video Generation
Acquisition of Spatially-Varying Reflectance and Surface Normals via Polarized Reflectance Fields
PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields
MaterialPicker: Multi-Modal Material Generation with Diffusion Transformers
Differentiable Inverse Rendering with Interpretable Basis BRDFs
NeISF++: Neural Incident Stokes Field for Polarized Inverse Rendering of Conductors and Dielectrics
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.