INR Toolkit

A comprehensive toolkit for Implicit Neural Representations (INRs) providing utilities for coordinate generation, dataset loading, and neural network training.

Code

Go to Code for more details.

References

Papers

General

• SIREN: Implicit Neural Representations with Periodic Activation Functions
• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
• WIRE: Wavelet Implicit Neural Representations
• Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey

3D reconstruction

• Occupancy Networks: Learning 3D Reconstruction in Function Space
• DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
• Implicit Geometric Regularization for Learning Shapes
• Shape As Points: A Differentiable Poisson Solver
• Occupancy-Based Dual Contouring
• Marching Neurons- Accurate Surface Extraction for Neural Implicit Shapes

Tutorials

• MICCAI 2024 Tutorial on Implicit Neural Representations for Medical Imaging: https://inr4miccai.github.io

Awesome Lists

• Awesome Implicit Neural Representations (from Prof.Vincent Sitzmann): Good resources for implicit neural representations

Courses

• 3DML Course - Implicit Neural Representations (from Prof. Minhyuk Sung)

Presentations

• TUM AI Lecture Series - Neural Implicit Representations for 3D Vision (Andreas Geiger): Link

SIREN

Both presentations are probably the same, but I just want to list them here for convenience:

• TUM AI Lecture Series - Implicit Neural Scene Representations (Vincent Sitzmann): Link (July 4, 2020)
• Talk @ Tübingen seminar series of the Autonomous Vision Group: Vincent Sitzmann: Implicit Neural Scene Representations: Link (July 7, 2020)