awesome-implicit-representations
github.com/vsitzmann/awesome-implicit-representations ↗A curated list of resources on implicit neural representations.
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DisclaimerWhy are they interesting?Implicit Neural Representations of GeometryImplicit representations of Geometry and AppearanceSymmetries in Implicit Neural RepresentationsHybrid implicit / explicit (condition implicit on local features)Learning correspondence with Neural Implicit RepresentationsRobotics ApplicationsGeneralization & Meta-Learning with Neural Implicit RepresentationsFitting high-frequency detail with positional encoding & periodic nonlinearitiesImplicit Neural Representations of ImagesComposing implicit neural representationsImplicit Representations for Partial Differential Equations & Boundary Value ProblemsGenerative Adverserial Networks with Implicit RepresentationsImage-to-image translationArticulated representations
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Robotics Applications
Generative Adverserial Networks with Implicit Representations
- Adversarial Generation of Continuous ImagesFor 2D
- Alias-Free GANFor 2D
- Alias-Free Generative Adversarial Networks (StyleGAN3)For 3D
- Generative Radiance Fields for 3D-Aware Image SynthesisFor 3D
- Image Generators with Conditionally-Independent Pixel SynthesisFor 2D
- Learning Continuous Image Representation with Local Implicit Image FunctionFor 2D
Articulated representations
- Andreas Geiger: Neural Implicit Representations for 3D Vision (Occupancy Networks, Texture Fields, Occupancy Flow, Differentiable Volumetric Rendering, GRAF)
- awesome-NeRF
List of implicit representations specifically on neural radiance fields (NeRF)
- Gerard Pons-Moll: Shape Representations: Parametric Meshes vs Implicit Functions
- NASA: Neural Articulated Shape Approximation
- Vincent Sitzmann: Implicit Neural Scene Representations (Scene Representation Networks, MetaSDF, Semantic Segmentation with Implicit Neural Representations, SIREN)
- Yaron Lipman: Implicit Neural Representations
Implicit Representations for Partial Differential Equations & Boundary Value Problems
Implicit Neural Representations of Images
Hybrid implicit / explicit (condition implicit on local features)
- Convolutional Occupancy Networks
- Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
- Implicit Functions in Feature Space for 3D ShapeReconstruction and Completion
- Local Deep Implicit Functions for 3D Shape
- Local Implicit Grid Representations for 3D Scenes
- Neural Sparse Voxel Fields
Implicit Neural Representations of Geometry
- DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
- IM-Net: Learning Implicit Fields for Generative Shape Modeling
- Neural Unsigned Distance Fields for Implicit Function Learning
- Occupancy Networks: Learning 3D Reconstruction in Function Space
- Sal: Sign agnostic learning of shapes from raw data
Implicit representations of Geometry and Appearance
- Deformable Neural Radiance FieldsFor dynamic scenes
- Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervisionFrom 2D supervision only (“inverse graphics”)
- D-NeRF: Neural Radiance Fields for Dynamic ScenesFor dynamic scenes
- Light Field Networks: Neural Scene Representations with Single-Evaluation RenderingFrom 2D supervision only (“inverse graphics”)
- Multiview neural surface reconstruction by disentangling geometry and appearanceFrom 2D supervision only (“inverse graphics”)
- Neural Radiance Flow for 4D View Synthesis and Video ProcessingFor dynamic scenes
Showing a sample of 64 resources. View the full list on GitHub →