fps(x: Tensor, batch: Optional[Tensor] = None, ratio: float = 0.5, random_start: bool = True, batch_size: Optional[int] = None) Tensor[source]

A sampling algorithm from the “PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space” paper, which iteratively samples the most distant point with regard to the rest points.

import torch
from torch_geometric.nn import fps

x = torch.tensor([[-1.0, -1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, 1.0]])
batch = torch.tensor([0, 0, 0, 0])
index = fps(x, batch, ratio=0.5)
  • x (torch.Tensor) – Node feature matrix \(\mathbf{X} \in \mathbb{R}^{N \times F}\).

  • batch (torch.Tensor, optional) – Batch vector \(\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N\), which assigns each node to a specific example. (default: None)

  • ratio (float, optional) – Sampling ratio. (default: 0.5)

  • random_start (bool, optional) – If set to False, use the first node in \(\mathbf{X}\) as starting node. (default: obj:True)

  • batch_size (int, optional) – The number of examples \(B\). Automatically calculated if not given. (default: None)

Return type: