models/boxnet.py [21:54]:
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    r"""
        A deep neural network for 3D object detection with end-to-end optimizable hough voting.

        Parameters
        ----------
        num_class: int
            Number of semantics classes to predict over -- size of softmax classifier
        num_heading_bin: int
        num_size_cluster: int
        input_feature_dim: (default: 0)
            Input dim in the feature descriptor for each point.  If the point cloud is Nx9, this
            value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors
        num_proposal: int (default: 128)
            Number of proposals/detections generated from the network. Each proposal is a 3D OBB with a semantic class.
        vote_factor: (default: 1)
            Number of votes generated from each seed point.
    """

    def __init__(self, num_class, num_heading_bin, num_size_cluster, mean_size_arr,
        input_feature_dim=0, num_proposal=128, vote_factor=1, sampling='vote_fps'):
        super().__init__()

        self.num_class = num_class
        self.num_heading_bin = num_heading_bin
        self.num_size_cluster = num_size_cluster
        self.mean_size_arr = mean_size_arr
        assert(mean_size_arr.shape[0] == self.num_size_cluster)
        self.input_feature_dim = input_feature_dim
        self.num_proposal = num_proposal
        self.vote_factor = vote_factor
        self.sampling=sampling

        # Backbone point feature learning
        self.backbone_net = Pointnet2Backbone(input_feature_dim=self.input_feature_dim)
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models/votenet.py [27:60]:
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    r"""
        A deep neural network for 3D object detection with end-to-end optimizable hough voting.

        Parameters
        ----------
        num_class: int
            Number of semantics classes to predict over -- size of softmax classifier
        num_heading_bin: int
        num_size_cluster: int
        input_feature_dim: (default: 0)
            Input dim in the feature descriptor for each point.  If the point cloud is Nx9, this
            value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors
        num_proposal: int (default: 128)
            Number of proposals/detections generated from the network. Each proposal is a 3D OBB with a semantic class.
        vote_factor: (default: 1)
            Number of votes generated from each seed point.
    """

    def __init__(self, num_class, num_heading_bin, num_size_cluster, mean_size_arr,
        input_feature_dim=0, num_proposal=128, vote_factor=1, sampling='vote_fps'):
        super().__init__()

        self.num_class = num_class
        self.num_heading_bin = num_heading_bin
        self.num_size_cluster = num_size_cluster
        self.mean_size_arr = mean_size_arr
        assert(mean_size_arr.shape[0] == self.num_size_cluster)
        self.input_feature_dim = input_feature_dim
        self.num_proposal = num_proposal
        self.vote_factor = vote_factor
        self.sampling=sampling

        # Backbone point feature learning
        self.backbone_net = Pointnet2Backbone(input_feature_dim=self.input_feature_dim)
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