(self, pclouds, samples, batch_size=100)
return np.vstack(nn_distances)
Convenience wrapper of self.get_nn_distance to get the chamfer distance between point clouds and samples
        Args:
            pclouds (N, K, 3) numpy array of N point clouds with K points each.
            samples (N, Ks, 3) numpy array of N point clouds with Ks points each, Ks <= K.
Convenience wrapper of self.get_nn_distance to get the chamfer distance between point clouds and samples
        Args:
            pclouds (N, K, 3) numpy array of N point clouds with K points each.
            samples (N, Ks, 3) numpy array of N point clouds with Ks points each, Ks <= K.
Convenience wrapper of self . get_nn_distance to get the chamfer distance between point clouds and samples Args : pclouds ( N K 3 ) numpy array of N point clouds with K points each . samples ( N Ks 3 ) numpy array of N point clouds with Ks points each Ks < = K .
def get_nn_distances(self, pclouds, samples, batch_size=100):
        """ Convenience wrapper of self.get_nn_distance to get the chamfer distance between point clouds and samples
        Args:
            pclouds (N, K, 3) numpy array of N point clouds with K points each.
            samples (N, Ks, 3) numpy array of N point clouds with Ks points each, Ks <= K.
        """
        nn_distances = []
        idx = np.arange(len(pclouds))
        for b in iterate_in_chunks(idx, batch_size):
            nn_distances.append(self.get_nn_distance(pclouds[b], samples[b]))
        return np.vstack(nn_distances)
def get_nn_distances ( self , pclouds , samples , batch_size = 100 ) : nn_distances = [ ] idx = np . arange ( len ( pclouds ) ) for b in iterate_in_chunks ( idx , batch_size ) : nn_distances . append ( self . get_nn_distance ( pclouds [ b ] , samples [ b ] ) ) return np . vstack ( nn_distances )