You could unsqueeze the pretrained kernels to create a new depth dimension and just repeat the values:
conv = nn.Conv2d(6, 12, 3)
weight = conv.weight
print(weight.shape)
> torch.Size([12, 6, 3, 3])

weight = weight.unsqueeze(2).repeat(1, 1, weight.size(2), 1, 1)
print(weight.shape)
> torch.Size([12, 6, 3, 3, 3])

conv3d = nn.Conv3d(6, 12, 3)
with torch.no_grad():
    conv3d.weight = nn.Parameter(weight)

It could be ambiguous, if the 2D kernels are not square kernels, but this depends on the pretrained model of course.