Method embed_DNeRF

script/models/nerfw.py:145–160

Nerfie paper section 3.5 Coarse-to-Fine Deformation Regularization

(self, inputs, epoch)

        return W_j

    def embed_DNeRF(self, inputs, epoch):
        ''' Nerfie paper section 3.5 Coarse-to-Fine Deformation Regularization '''
        # get weight for each frequency band j
        W_j = self.get_embed_weight(epoch, self.N_freqs, self.N) # W_j: [W_0, W_1, W_2, ..., W_{m-1}]
        
        # Fourier embedding
        out = []
        for fn in self.embed_fns: # 17, embed_fns:[input, cos, sin, cos, sin, ..., cos, sin]
            out.append(fn(inputs))

        # apply weighted positional encoding, only to cos&sins
        for i in range(len(W_j)):
            out[2*i+1] = W_j[i] * out[2*i+1]
            out[2*i+2] = W_j[i] * out[2*i+2]
        ret = torch.cat(out, -1)
        return ret

    def update_N(self, N):
        self.N=N