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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)
Revision as of 15:22, 30 August 2021
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Links [https://arxiv.org/abs/2106.02634 https://arxiv.org/abs/2106.02634] | Links [https://arxiv.org/abs/2106.02634 https://arxiv.org/abs/2106.02634] | ||
== | ==Method== | ||
===Background=== | ===Background=== | ||
See NeRF and SIREN. | See NeRF and SIREN. | ||
=== | ===Light Field Networks=== | ||
The idea here is to use light field rendering instead of volume rendering or SDF ray marching.<br> | The idea here is to use light field rendering instead of volume rendering or SDF ray marching.<br> | ||
In this case, the input to the network is still a single point and direction but it represents the entire ray rather than the radiance at a particular point.<br> | In this case, the input to the network is still a single point and direction but it represents the entire ray rather than the radiance at a particular point.<br> | ||
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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> | ||
===Metalearning=== | ===Metalearning=== | ||