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This talk examines implicit neural representations for signals including images, audio, video, and physical fields, with emphasis on neural scene representations. It explains neural rendering, learned priors, single-image 3D reconstruction, and meta-learning for fast inference.
Syllabus
Introduction
Implicit Neural Representation
Why does that not work
Sinusoidal Representation Networks
Audio Signals
Scene Reconstruction
Different Models
Deep Boxes
Implicit Mule Representation
Mule Renderer
Learning Priors
Few Shot Reconstruction
Generalizing
Complex Scenes
Related 3D Scenes
AutoDecoder
Meta SDF Fitness
Test Time
Comparison
Distance Functions
Semisupervised Approach
Recap
Future work
Acknowledgements
Taught by
Andreas Geiger