- pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
- Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
- Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
- SAL: Sign Agnostic Learning of Shapes from Raw Data
- Neural Sparse Voxel Fields
- D-NeRF: Neural Radiance Fields for Dynamic Scenes
- Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
- MetaSDF: Meta-learning Signed Distance Functions
- Implicit Geometric Regularization for Learning Shapes
- PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
- SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
- NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
- Neural Unsigned Distance Fields for Implicit Function Learning
- Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
- Implicit Neural Representations with Periodic Activation Functions
- Adversarial Generation of Continuous Images
- Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion
- PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations
- Compositional Pattern Producing Networks: A Novel Abstraction of Development
- Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
- Unconstrained Scene Generation with Locally Conditioned Radiance Fields
- DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
- Local Implicit Grid Representations for 3D Scenes
- GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
- Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
- Semantic Implicit Neural Scene RepresentationsWith Semi-Supervised Training
- Learning Implicit Fields for Generative Shape Modeling
- NASA: Neural Articulated Shape Approximation
- Convolutional Occupancy Networks
- Texture Fields: Learning Texture Representations in Function Space
- GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
- Learning Continuous Image Representation with Local Implicit Image Function
- Occupancy Networks: Learning 3D Reconstruction in Function Space
- Object-Centric Neural Scene Rendering
- Local Deep Implicit Functions for 3D Shape
- Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes
- Image Generators with Conditionally-Independent Pixel Synthesis
- AutoInt: Automatic Integration for Fast Neural Volume Rendering
- Learned Initializations for Optimizing Coordinate-Based Neural Representations
- Spatially-Adaptive Pixelwise Networks for Fast Image Translation
- Neural Radiance Flow for 4D View Synthesis and Video Processing
- Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video
- Nerfies: Deformable Neural Radiance Fields
- X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation
- Space-time Neural Irradiance Fields for Free-Viewpoint Video
manjunath5496/Implicit-Neural-Representation-Papers
"The academic world was marching toward ever more specialized knowledge, expressed in ever more dense jargon."― Michael Crichton