INNER CODE UNIT · Python

c1

yashbhalgat/HashNeRF-pytorch · hash_encoding.py:51

        c1 = c01*(1-weights[:,1][:,None]) + c11*weights[:,1][:,None]

        # step 3
        c = c0*(1-weights[:,2][:,None]) + c1*weights[:,2][:,None]

        return c

    def forward(self, x):
        # x is 3D point position: B x 3
        x_embedded_all = []
        for i in range(self.n_levels):
            resolution = torch.floor(self.base_resolution * self.b**i)
            voxel_min_vertex, voxel_max_vertex, hashed_voxel_indices, keep_mask = get_voxel_vertices(\
                                                x, self.bounding_box, \
                                                resolution, self.log2_hashmap_size)
            
            voxel_embedds = self.embeddings[i](hashed_voxel_indices)

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