kaolin.metrics.pointcloud

API

kaolin.metrics.pointcloud.chamfer_distance(p1, p2, w1=1.0, w2=1.0)

Computes the chamfer distance between two pointclouds

Parameters

  • p1 (torch.Tensor) – pointclouds of shape (B, N, 3)

  • p2 (torch.Tensor) – pointclouds of shape (B, M, 3)

  • w1 (float) – weighting of forward direction. Default: 1.

  • w2 (float) – weighting of backward direction. Default: 1.

Returns

chamfer distance between two pointclouds p1 and p2 of shape (B)

Return type

(torch.Tensor)

Example

>>> p1 = torch.tensor([[[8.8977, 4.1709, 1.2839],
...                     [8.5640, 7.7767, 9.4214]],
...                    [[0.5431, 6.4495, 11.4914],
...                     [3.2126, 8.0865, 3.1018]]], device='cuda', dtype=torch.float)
>>> p2 = torch.tensor([[[6.9340, 6.1152, 3.4435],
...                     [0.1032, 9.8181, 11.3350]],
...                    [[11.4006, 2.2154, 7.9589],
...                     [4.2586, 1.4133, 7.2606]]], device='cuda', dtype=torch.float)
>>> chamfer_distance(p1, p2)
tensor([ 72.5838, 151.0809], device='cuda:0')
kaolin.metrics.pointcloud.f_score(gt_points, pred_points, radius=0.01, eps=1e-08)

Computes the f-score of two sets of points, with a hit defined by two point existing within a defined radius of each other

Parameters

  • gt_points (torch.Tensor) – ground truth pointclouds of shape (B, N, 3)

  • pred_points (torch.Tensor) – predicted points pointclouds of shape (B, M, 3)

  • radius (float) – radius from a point to define a hit Default: 0.01

  • eps (float) – epsilon used to calculate f score.

Returns

computed f-score tensor of shape (B), which has the same dtype as input pred_points.

Return type

(torch.Tensor)

Example

>>> p1 = torch.tensor([[[8.8977, 4.1709, 1.2839],
...                     [8.5640, 7.7767, 9.4214]],
...                    [[0.5431, 6.4495, 11.4914],
...                     [3.2126, 8.0865, 3.1018]]], device='cuda', dtype=torch.float)
>>> p2 = torch.tensor([[[9.4863, 4.2249, 0.1712],
...                     [8.1783, 8.5310, 8.5119]],
...                    [[-0.0020699, 6.4429, 12.3],
...                     [3.8386, 8.3585, 4.7662]]], device='cuda', dtype=torch.float)
>>> f_score(p1, p2, radius=1)
tensor([0.5000, 0.0000], device='cuda:0')
>>> f_score(p1, p2, radius=1.5)
tensor([1.0000, 0.5000], device='cuda:0')
kaolin.metrics.pointcloud.sided_distance(p1, p2)

For every point in p1 find the indices and euclidean distances of the closest point in p2.

Parameters
Returns

the indices and distances from points in p1 to the corresponding closest points in p2, both have shape of (B, N)

Return type

(torch.Tensor, torch.Tensor)

Example

>>> p1 = torch.tensor([[[5.9336, 4.9742, 8.1047]],
...                    [[4.1939, 3.3612, 9.5407]]], device='cuda', dtype=torch.float)
>>> p2 = torch.tensor([[[1.6998, 0.7719, 2.9987],
...                     [0.1812, 8.9342, 10.0285]],
...                    [[10.0184, 0.3928, 5.2545],
...                     [4.2934, 11.2127, 4.5247]]], device='cuda', dtype=torch.float)
>>> distance, idx = sided_distance(p1, p2)
>>> distance
tensor([[52.4727],
        [61.1077]], device='cuda:0')
>>> idx
tensor([[1],
        [0]], device='cuda:0')