Recommand Reading:

• Implicit Neural Representations with Periodic Activation Functions (Sitzmann et al. 2020)
• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding, Müller et al., SIGGRAPH 2022 | github | bibtex
• Neural Radiance Fields (NeRF)
• A Structured Dictionary Perspective on Implicit Neural Representations
• Geometry Processing with Neural Fields

Latest:

• Regularize implicit neural representation by itself
• Transformers as Meta-Learners for Implicit Neural Representations
• Versatile Neural Processes for Learning Implicit Neural Representations
• Efficient Meta-Learning via Error-based Context Pruning for Implicit Neural Representations
• Generalizable Implicit Neural Representations via Instance Pattern Composers
• Transformers as Meta-Learners for Implicit Neural Representations
• Deep Learning on Implicit Neural Representations of Shapes
• Neural Implicit Dictionary Learning via Mixture-of-Expert Training
• Implicit Neural Representations with Levels-of-Experts
• Neural Implicit Surface Evolution using Differential Equations
• A Level Set Theory for Neural Implicit Evolution under Explicit Flows
• Coordinates Are NOT Lonely -- Codebook Prior Helps Implicit Neural 3D Representations
• Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
• Continuous PDE Dynamics Forecasting with Implicit Neural Representations
• Implicit Neural Spatial Representations for Time-dependent PDEs
• DiGS : Divergence guided shape implicit neural representation for unoriented point clouds
• Hindering Adversarial Attacks with Implicit Neural Representations

3D Representation History

• DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation (Park et al. 2019)
• Occupancy Networks: Learning 3D Reconstruction in Function Space (Mescheder et al. 2019)
• IM-Net: Learning Implicit Fields for Generative Shape Modeling (Chen et al. 2018)
• Sal: Sign agnostic learning of shapes from raw data (Atzmon et al. 2019)
• Implicit Geometric Regularization for Learning Shapes (Gropp et al. 2020)
• Implicit Neural Representations with Periodic Activation Functions (Sitzmann et al. 2020)
• Neural Unsigned Distance Fields for Implicit Function Learning (Chibane et al. 2020)

Implicit representations of Geometry and Appearance(NeRF Related)

• Neural Radiance Fields (NeRF) (For a curated list of NeRF follow-up work specifically, From awesome-NeRF

Hybrid Representation (implicit / explicit)

• Implicit Functions in Feature Space for 3D ShapeReconstruction and Completion
• Local Implicit Grid Representations for 3D Scenes
• Convolutional Occupancy Networks
• Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
• Neural Sparse Voxel Fields
• Pixel-NERF (Yu et al. 2020) :
• Local Deep Implicit Functions for 3D Shape
• PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations

• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding, Müller et al., SIGGRAPH 2022 | github | bibtex
• Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Theory

Symmetries in Implicit Neural Representations

• Vector Neurons: A General Framework for SO(3)-Equivariant Networks (Deng et al. 2021)

Generalization & Meta-Learning with Neural Implicit Representations

• Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization (Saito et al. 2019)
• Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations (Sitzmann et al. 2019)
• MetaSDF: MetaSDF: Meta-Learning Signed Distance Functions (Sitzmann et al. 2020)
• SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images (Lin et al. 2020)
• Learned Initializations for Optimizing Coordinate-Based Neural Representations (Tancik et al. 2020)

Fitting high-frequency detail with positional encoding & periodic nonlinearities

• Implicit Neural Representations with Periodic Activation Functions (Sitzmann et al. 2020)
• Fourier features let networks learn high frequency functions in low dimensional domains (Tancik et al. 2020)

Applications

Robotics Applications

• 3D Neural Scene Representations for Visuomotor Control
• Full-Body Visual Self-Modeling of Robot Morphologies
• Neural Descriptor Fields: SE(3)-Equvariant Object Representations for Manipulation

Implicit Neural Representations of Images

• Implicit Neural Representations with Periodic Activation Functions (Sitzmann et al. 2020)
• X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation (Bemana et al. 2020)
• Learning Continuous Image Representation with Local Implicit Image Function (Chen et al. 2020)
• Alias-Free Generative Adversarial Networks (StyleGAN3)

Implicit Representations for Partial Differential Equations & Boundary Value Problems

• Implicit Geometric Regularization for Learning Shapes (Gropp et al. 2020)
• Implicit Neural Representations with Periodic Activation Functions (Sitzmann et al. 2020)
• AutoInt: Automatic Integration for Fast Neural Volume Rendering (Lindell et al. 2020)
• MeshfreeFlowNet: Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework (Jiang et al. 2020)

Generative Adverserial Networks with Implicit Representations

For 3D

• Generative Radiance Fields for 3D-Aware Image Synthesis (Schwarz et al. 2020)
• pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis (Chan et al. 2020)
• Unconstrained Scene Generation with Locally Conditioned Radiance Fields (DeVries et al. 2021)
• Alias-Free Generative Adversarial Networks (StyleGAN3)

For 2D

• Adversarial Generation of Continuous Images (Skorokhodov et al. 2020)
• Learning Continuous Image Representation with Local Implicit Image Function (Chen et al. 2020)
• Image Generators with Conditionally-Independent Pixel Synthesis (Anokhin et al. 2020)
• Alias-Free GAN (Karras et al. 2021)

Image-to-image translation

• Spatially-Adaptive Pixelwise Networks for Fast Image Translation (Shaham et al. 2020)

Articulated representations

• NASA: Neural Articulated Shape Approximation (Deng et al. 2020)