chamfer_pcd
import tensorflow as tf import numpy as np def distance_matrix(array1, array2): """ arguments: array1: the array, size: (num_point, num_feature) array2: the samples, size: (num_point, num_feature) returns: distances: each entry is the distance from a sample to array1 , it's size: (num_point, num_point) """ num_point, num_features = array1.shape expanded_array1 = tf.tile(array1, (num_point, 1)) expanded_array2 = tf.reshape( tf.tile(tf.expand_dims(array2, 1), (1, num_point, 1)), (-1, num_features)) distances = tf.norm(expanded_array1-expanded_array2, axis=1) distances = tf.reshape(distances, (num_point, num_point)) return distances def av_dist(array1, array2): """ arguments: array1, array2: both size: (num_points, num_feature) returns: distances: size: (1,) """ distances = distance_matrix(array1, array2) distances = tf.reduce_min(distances, axis=1) distances = tf.reduce_mean(distances) return distances def av_dist_sum(arrays): """ arguments: arrays: array1, array2 returns: sum of av_dist(array1, array2) and av_dist(array2, array1) """ array1, array2 = arrays av_dist1 = av_dist(array1, array2) av_dist2 = av_dist(array2, array1) return av_dist1+av_dist2 def chamfer_distance_tf(array1, array2): batch_size, num_point, num_features = array1.shape dist = tf.reduce_mean( tf.map_fn(av_dist_sum, elems=(array1, array2), dtype=tf.float64) ) return dist def array2samples_distance(array1, array2): """ arguments: array1: the array, size: (num_point, num_feature) array2: the samples, size: (num_point, num_feature) returns: distances: each entry is the distance from a sample to array1 """ num_point, num_features = array1.shape expanded_array1 = np.tile(array1, (num_point, 1)) expanded_array2 = np.reshape( np.tile(np.expand_dims(array2, 1), (1, num_point, 1)), (-1, num_features)) distances = np.linalg.norm(expanded_array1-expanded_array2, axis=1) distances = np.reshape(distances, (num_point, num_point)) distances = np.min(distances, axis=1) distances = np.mean(distances) return distances def chamfer_distance_numpy(array1, array2): batch_size, num_point, num_features = array1.shape dist = 0 for i in range(batch_size): av_dist1 = array2samples_distance(array1[i], array2[i]) av_dist2 = array2samples_distance(array2[i], array1[i]) dist = dist + (av_dist1+av_dist2)/batch_size return dist if __name__=='__main__': batch_size = 3 num_point = 10 num_features = 3 np.random.seed(1) array1 = np.random.randint(0, high=4, size=(batch_size, num_point, num_features)) array2 = np.random.randint(0, high=4, size=(batch_size, num_point, num_features)) print (array1) #print(array2) print('numpy: ', chamfer_distance_numpy(array1, array2))