(self, pclouds, batch_size=50)
return np.vstack(projected_samples), np.vstack(projected_idx)
Convenience wrapper of self.get_sample to get the samples for a set of input point clouds.
        Args:
            pclouds (N, K, 3) numpy array of N point clouds with K points each.
            batch_size size of point clouds batch
Convenience wrapper of self.get_sample to get the samples for a set of input point clouds.
        Args:
            pclouds (N, K, 3) numpy array of N point clouds with K points each.
            batch_size size of point clouds batch
Convenience wrapper of self . get_sample to get the samples for a set of input point clouds . Args : pclouds ( N K 3 ) numpy array of N point clouds with K points each . batch_size size of point clouds batch
def get_samples(self, pclouds, batch_size=50):
        """ Convenience wrapper of self.get_sample to get the samples for a set of input point clouds.
        Args:
            pclouds (N, K, 3) numpy array of N point clouds with K points each.
            batch_size size of point clouds batch
        """
        projected_samples = []
        projected_idx = []
        idx = np.arange(len(pclouds))
        for b in iterate_in_chunks(idx, batch_size):
            prj, prj_idx = self.get_sample(pclouds[b])
            projected_samples.append(prj)
            projected_idx.append(prj_idx)
        return np.vstack(projected_samples), np.vstack(projected_idx)
def get_samples ( self , pclouds , batch_size = 50 ) : projected_samples = [ ] projected_idx = [ ] idx = np . arange ( len ( pclouds ) ) for b in iterate_in_chunks ( idx , batch_size ) : prj , prj_idx = self . get_sample ( pclouds [ b ] ) projected_samples . append ( prj ) projected_idx . append ( prj_idx ) return np . vstack ( projected_samples ) , np . vstack ( projected_idx )