Pointnet_Pointnet2_pytorch

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class get_model(nn.Module):
    def __init__(self, num_classes, normal_channel=False):
        super(get_model, self).__init__()
        ...
        self.sa1 = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [32, 64, 128], 3+additional_channel, [[32, 32, 64], [64, 64, 128], [64, 96, 128]])
        self.sa2 = PointNetSetAbstractionMsg(128, [0.4,0.8], [64, 128], 128+128+64, [[128, 128, 256], [128, 196, 256]])
        self.sa3 = PointNetSetAbstraction(npoint=None, radius=None, nsample=None, in_channel=512 + 3, mlp=[256, 512, 1024], group_all=True)
        ...

    def forward(self, xyz, cls_label):
        # Set Abstraction layers
        B,C,N = xyz.shape
        if self.normal_channel:
            l0_points = xyz
            l0_xyz = xyz[:,:3,:]
        else:
            l0_points = xyz
            l0_xyz = xyz
        l1_xyz, l1_points = self.sa1(l0_xyz, l0_points)
        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)
        l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)

To the PointNetSetAbstractionMsg.forward(xyz, points), xyz is input points position data with [B, C, N], and points is input points data with [B, D, N]. Then why l0_points in such code above is xyz but not xyz[3: 3+D] ??????