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Intro
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Neural Radiance Fields for View Synthesis
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- 1 Intro
- 2 The problem of novel view interpolation
- 3 GB-alpha volume rendering for view synthesis
- 4 eural networks as a continuous shape representation
- 5 Neural network replaces large N-d array
- 6 enerate views with traditional volume rendering
- 7 igma parametrization for continuous opacity
- 8 Two pass rendering: coarse
- 9 Two pass rendering: fine
- 10 Viewing directions as input
- 11 Volume rendering is trivially differentiable
- 12 ptimize with gradient descent on rendering loss
- 13 HeRF encodes convincing view-dependent effects using directional dependence
- 14 NeRF encodes detailed scene geometry
- 15 Going forward
- 16 Fourier Features Let Networks Learn
- 17 Input Mapping
- 18 Key Points
- 19 sing kernel regression to approximate deep networks
- 20 TK: modeling deep network as kernel regression
- 21 Sinusoidal mapping results in a composed stationary NTK
- 22 Resulting composed NTK is stationary
- 23 No-mapping NTK clearly not stationary
- 24 Toy example of stationarity in practice
- 25 Modifying mapping manipulates kernel spectrum
- 26 ernel spectrum has dramatic effect on convergence and generalization
- 27 requency sampling distribution bandwidth matter more than shape
- 28 Mapping Code
- 29 2D Images
- 30 3D Shape
- 31 Indirect supervision tasks Ground Truth
