engelen

/**
 * Retrieve the array key corresponding to the largest element in the array.
 *
 * @param {Array.<number>} array Input array
 * @return {number} Index of array element with largest value
 */
function argMax(array) {
  return array.map((x, i) => [x, i]).reduce((r, a) => (a[0] > r[0] ? a : r))[1];
}

mkocabas

import numpy as np
import torch

def compute_similarity_transform(S1, S2):
    '''
    Computes a similarity transform (sR, t) that takes
    a set of 3D points S1 (3 x N) closest to a set of 3D points S2,
    where R is an 3x3 rotation matrix, t 3x1 translation, s scale.
    i.e. solves the orthogonal Procrutes problem.
    '''

sergeyprokudin

import numpy as np
from sklearn.neighbors import NearestNeighbors


def chamfer_distance(x, y, metric='l2', direction='bi'):
    """Chamfer distance between two point clouds

    Parameters
    ----------
    x: numpy array [n_points_x, n_dims]