Use Pytorch to calculate Chamfer distance

Chamfer_Distance_Pytorch.py

import torch


def chamfer_distance_without_batch(p1, p2, debug=False):

    '''
    Calculate Chamfer Distance between two point sets
    :param p1: size[1, N, D]
    :param p2: size[1, M, D]
    :param debug: whether need to output debug info
    :return: sum of Chamfer Distance of two point sets
    '''

    assert p1.size(0) == 1 and p2.size(0) == 1
    assert p1.size(2) == p2.size(2)

    if debug:
        print(p1[0][0])

    p1 = p1.repeat(p2.size(1), 1, 1)
    if debug:
        print('p1 size is {}'.format(p1.size()))

    p1 = p1.transpose(0, 1)
    if debug:
        print('p1 size is {}'.format(p1.size()))
        print(p1[0])

    p2 = p2.repeat(p1.size(0), 1, 1)
    if debug:
        print('p2 size is {}'.format(p2.size()))
        print(p2[0])

    dist = torch.add(p1, torch.neg(p2))
    if debug:
        print('dist size is {}'.format(dist.size()))
        print(dist[0])

    dist = torch.norm(dist, 2, dim=2)
    if debug:
        print('dist size is {}'.format(dist.size()))
        print(dist)

    dist = torch.min(dist, dim=1)[0]
    if debug:
        print('dist size is {}'.format(dist.size()))
        print(dist)

    dist = torch.sum(dist)
    if debug:
        print('-------')
        print(dist)

    return dist


def chamfer_distance_with_batch(p1, p2, debug):

    '''
    Calculate Chamfer Distance between two point sets
    :param p1: size[B, N, D]
    :param p2: size[B, M, D]
    :param debug: whether need to output debug info
    :return: sum of all batches of Chamfer Distance of two point sets
    '''

    assert p1.size(0) == p2.size(0) and p1.size(2) == p2.size(2)

    if debug:
        print(p1[0])

    p1 = p1.unsqueeze(1)
    p2 = p2.unsqueeze(1)
    if debug:
        print('p1 size is {}'.format(p1.size()))
        print('p2 size is {}'.format(p2.size()))
        print(p1[0][0])

    p1 = p1.repeat(1, p2.size(2), 1, 1)
    if debug:
        print('p1 size is {}'.format(p1.size()))

    p1 = p1.transpose(1, 2)
    if debug:
        print('p1 size is {}'.format(p1.size()))
        print(p1[0][0])

    p2 = p2.repeat(1, p1.size(1), 1, 1)
    if debug:
        print('p2 size is {}'.format(p2.size()))
        print(p2[0][0])

    dist = torch.add(p1, torch.neg(p2))
    if debug:
        print('dist size is {}'.format(dist.size()))
        print(dist[0])

    dist = torch.norm(dist, 2, dim=3)
    if debug:
        print('dist size is {}'.format(dist.size()))
        print(dist)

    dist = torch.min(dist, dim=2)[0]
    if debug:
        print('dist size is {}'.format(dist.size()))
        print(dist)

    dist = torch.sum(dist)
    if debug:
        print('-------')
        print(dist)

    return dist

if __name__ == '__main__':
    import numpy as np

    p1 = torch.from_numpy(np.array([
        [[1., 2, 3], [4, 5, 6], [3, 5, 6], [5, 6, 7]],
        [[2., 2, 3], [3, 5, 6], [4, 5, 6], [8, 6, 7]]
                                    ]))
    p2 = torch.from_numpy(np.array([
        [[3., 7, 8], [1, 4, 5]],
        [[3., 8, 8], [2, 4, 5]]
    ]))

    # print('p1 size is {}, p2 size is {}'.format(p1.size(), p2.size()))

    print(chamfer_distance_with_batch(p1, p2, False))

    p1_1 = torch.from_numpy(np.array([
        [[1., 2, 3], [4, 5, 6], [3, 5, 6], [5, 6, 7]],
    ]))
    p1_2 = torch.from_numpy(np.array([
        [[2., 2, 3], [3, 5, 6], [4, 5, 6], [8, 6, 7]]
    ]))

    p2_1 = torch.from_numpy(np.array([
        [[3., 7, 8], [1, 4, 5]],
    ]))
    p2_2 = torch.from_numpy(np.array([
        [[3., 8, 8], [2, 4, 5]]
    ]))
    print(torch.add(chamfer_distance_without_batch(p1_1, p2_1), chamfer_distance_without_batch(p1_2, p2_2)))

Thank you! This way I didn't have to deal with installing pytorch3d or kaolin... Saved some good amount of time.