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Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering: Difference between revisions
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering (view source)
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The benefit is that Plucker coordinates are invariance to the selected point and can represent the entire 360 set of rays.<br> | The benefit is that Plucker coordinates are invariance to the selected point and can represent the entire 360 set of rays.<br> | ||
<math>\mathbf{r} = (\mathbf{d},\mathbf{m}) \in \mathbb{R}^6</math> where <math>\mathbf{m}=\mathbf{p} \times \mathbf{d}</math> | <math>\mathbf{r} = (\mathbf{d},\mathbf{m}) \in \mathbb{R}^6</math> where <math>\mathbf{m}=\mathbf{p} \times \mathbf{d}</math> | ||
===Geometry=== | |||
(NOT FILLED IN)<br> | |||
There is some interesting discussion in the paper about the point-line isomorphism, epipolar plane image, and how to extract depth. | |||
===Metalearning=== | ===Metalearning=== | ||
They use a hypernetwork to convert latent codes to scenes represented by the networks. | |||
==Evaluation== | |||