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Monocular Neural Image Based Rendering with Continuous View Control: Difference between revisions
Monocular Neural Image Based Rendering with Continuous View Control (view source)
Revision as of 03:53, 14 June 2020
, 14 June 2020→Architecture
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The encoder converts images into latent points. | The encoder converts images into latent points. | ||
It consists of | It consists of 8 convolutional blocks which each downsample the feature map. (Note that the paper says 7 but their code uses 8) | ||
Each block is: Conv-BatchNorm-LeakyReLU. | Each block is: Conv-BatchNorm-LeakyReLU. | ||
The output of the convolutional blocks are put through a fully connected layer and reshaped into a \(200 \times 3\) matrix. | The output of the convolutional blocks are put through a fully connected layer and reshaped into a \(200 \times 3\) matrix. | ||
The decoder renders the latent points into a depth map from the target view. | The decoder renders the latent points into a depth map from the target view. | ||
It consists of | It consists of 8 blocks of: Upsample-Conv-BatchNorm-LeakyReLU. | ||
They use bilinear upsampling. | They use bilinear upsampling. | ||
* Optimizer: Adam | |||
** learning_rate=0.00006, beta_1=0.5, beta_2=0.999 | |||
==Evaluation== | ==Evaluation== | ||