Awesome-InverseRendering
List of awesome papers on Intrinsic Decomposition & Inverse Rendering
https://github.com/tkuri/Awesome-InverseRendering
Last synced: 12 days ago
JSON representation
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Dataset
- Intrinsic Images in the Wild
- Shading Annotations in the Wild
- A Dataset of Multi-Illumination Images in the Wild
- OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets - openrooms.github.io/)|
- Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding - hypersim)|
- Intrinsic Images in the Wild
- Shading Annotations in the Wild
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Intrinsic Decomposition (DL)
- Intrinsic Decompositions for Image Editing
- Intrinsic Image Transformation via Scale Space Decomposition
- Revisiting Deep Intrinsic Image Decompositions
- Learning Intrinsic Image Decomposition from Watching the World - learning-intrinsic-images) / [Dataset](https://research.cs.cornell.edu/bigtime/)|
- Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences
- CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering
- Single Image Intrinsic Decomposition without a Single Intrinsic Image
- InverseRenderNet: Learning single image inverse rendering
- Learning to Separate Multiple Illuminants in a Single Image
- GLoSH: Global-Local Spherical Harmonics for Intrinsic Image Decomposition
- Unsupervised Learning for Intrinsic Image Decomposition from a Single Image
- PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition - Net)|
- Neural Inverse Rendering of an Indoor Scene From a Single Image
- Self-Supervised Intrinsic Image Decomposition - network) / [Dataset](https://www.shapenet.org/)|
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Intrinsic Decomposition (non-DL)
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Inverse Rendering (DL)
- Reflectance Modeling by Neural Texture Synthesis
- Deep Outdoor Illumination Estimation
- Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks - augmented-net)||✓|✓|
- Reflectance Capture using Univariate Sampling of BRDFs
- Learning to Predict Indoor Illumination from a Single Image
- Single-Image SVBRDF Capture with a Rendering-Aware Deep Network - sam.inria.fr/fungraph/deep-materials/)||✓|✓|
- Learning to Estimate Indoor Lighting from 3D Objects
- Relighting Humans: Occlusion-Aware Inverse Rendering for Full-Body Human Images
- Learning to Reconstruct Shape and Spatially-Varying Reflectance from a Single Image
- Fast Spatially-Varying Indoor Lighting Estimation
- All-Weather Deep Outdoor Lighting Estimation
- DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality
- Neural Illumination: Lighting Prediction for Indoor Environments
- Deep Parametric Indoor Lighting Estimation
- Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image - openrooms.github.io/)|✓|✓|✓|
- Object-Based Illumination Estimation with Rendering-Aware Neural Networks
- Single-Shot Neural Relighting and SVBRDF Estimation
- MaterialGAN: Reflectance Capture using a Generative SVBRDF Model
- Highlight-Aware Two-Stream Network for Single-Image SVBRDF Acquisition
- Deep Polarization Imaging for 3D Shape and SVBRDF Acquisition
- Shape and Material Capture at Home
- SurfaceNet: Adversarial SVBRDF Estimation from a Single Image
- EMLight: Lighting Estimation via Spherical Distribution Approximation
- Material and Lighting Reconstruction for Complex Indoor Scenes with Texture-space Differentiable Rendering
- Generative Modelling of BRDF Textures from Flash Images
- IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes - UCSD/IRISformer)|✓|✓|✓|
- Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
- PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting - 46/PhySG)|✓|✓|✓|
- Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting
- Relighting Humans: Occlusion-Aware Inverse Rendering for Full-Body Human Images
- Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
- Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image - openrooms.github.io/)|✓|✓|✓|
- Material and Lighting Reconstruction for Complex Indoor Scenes with Texture-space Differentiable Rendering
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Inverse Rendering (non-DL)
- Appearance-from-Motion: Recovering Spatially Varying Surface Reflectance under Unknown Lighting
- Mobile Surface Reflectometry
- Simultaneous Acquisition of Microscale Reflectance and Normals
- Recovering Shape and Spatially-Varying Surface Reflectance under Unknown Illumination
- Polarization imaging reflectometry in the wild
- Simultaneous Acquisition of Polarimetric SVBRDF and Normals
- Progressive Acquisition of SVBRDF and Shape in Motion
- Manifold bootstrapping for SVBRDF capture
- Practical SVBRDF Capture in the Frequency Domain
- Two-Shot SVBRDF Capture for Stationary Materials
- Simultaneous Acquisition of Microscale Reflectance and Normals
- Simultaneous Acquisition of Polarimetric SVBRDF and Normals
- Progressive Acquisition of SVBRDF and Shape in Motion
- Appearance-from-Motion: Recovering Spatially Varying Surface Reflectance under Unknown Lighting
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Portrait Relighting (DL)
- Single Image Portrait Relighting
- Learning Physics-Guided Face Relighting Under Directional Light
- Relighting Images in the Wild with a Self-Supervised Siamese Auto-Encoder
- Monocular Reconstruction of Neural Face Reflectance Fields
- Neural Video Portrait Relighting in Real-time via Consistency Modeling - Video-Portrait-Relighting-in-Real-time-via-Consistency-Modeling) / [Dataset](https://zhanglongwen.com/projects/nvpr/dataset.html)|
- Single Image Portrait Relighting via Explicit Multiple Reflectance Channel Modeling
- Learning to Relight Portraits for Background Replacement