Ne RF: Representing Scenes as Neural Radiance Fields for View Synthesis ( ML Research Paper Explained)

, nerf, neuralrendering, deeplearning View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential volume rendering procedure, and achieves stateoftheart view synthesis. It includes directional dependence and is able to capture fine structural details, as well as reflection effects and transparency. OUTLINE: 0:00 Intro Overview 4:50 View Synthesis Task Description 5:50 The fundamental difference to classic Deep Learning 7:00 NeRF Core Concept 15:30 Training the NeRF from sparse views 20:50 Radiance Field Volume Rendering 23:20 Resulting View Dependence 24:00 Positional Encoding 28:00 Hierarchical Volume Sampling 30:15 Experimental Results 33:30 Comments Conclusion Paper: Website Code: My Video on SIREN: Abstract: We