provider.py

import numpy as np


def shift_point_cloud(batch_data, shift_range=0.1):
    """ Randomly shift point cloud. Shift is per point cloud.
        Input:
          BxNx3 array, original batch of point clouds
        Return:
          BxNx3 array, shifted batch of point clouds
    """
    B, N, C = batch_data.shape
    shifts = np.random.uniform(-shift_range, shift_range, (B, 3))
    for batch_index in range(B):
        batch_data[batch_index, :, :] += shifts[batch_index, :]
    return batch_data


def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25):
    """ Randomly scale the point cloud. Scale is per point cloud.
        Input:
            BxNx3 array, original batch of point clouds
        Return:
            BxNx3 array, scaled batch of point clouds
    """
    B, N, C = batch_data.shape
    scales = np.random.uniform(scale_low, scale_high, B)
    for batch_index in range(B):
        batch_data[batch_index, :, :] *= scales[batch_index]
    return batch_data


def random_point_dropout(batch_pc, max_dropout_ratio=0.875):
    """ batch_pc: BxNx3 """
    for b in range(batch_pc.shape[0]):
        dropout_ratio = np.random.random()*max_dropout_ratio   # 0~0.875
        drop_idx = np.where(np.random.random((batch_pc.shape[1])) <= dropout_ratio)[0]
        if len(drop_idx) > 0:
            batch_pc[b, drop_idx, :] = batch_pc[b, 0, :]    # set to the first point
    return batch_pc