Understanding the NeRF model architecture

So far, we have used the NeRF model class without fully knowing what it looks like. In this section, we will first visualize what the neural network looks like and then go through the code in detail and understand how it is implemented.

The neural network takes the harmonic embedding of the spatial location (x, y, z) and the harmonic embedding of (θ, ∅) as its input and outputs the predicted density σ and the predicted color (r, g, b). The following figure illustrates the network architecture that we are going to implement in this section:

Figure 6.5: The simplified model architecture of the NeRF model

Note

The model architecture that we are going to implement is different from the original NeRF model architecture. In this implementation, we are implementing a simplified version of it. This simplified architecture makes it faster and easier to train.

Let us start defining the NeuralRadianceField...