You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Given input points \{x_{1},x_{2},...,x_{n}\}, we use iterative farthest point sampling (FPS) to choose a subset of points \{x_{i_{1}},x_{i_{2}},...,x_{i_{m}}\}, such that x_{i_{j}} is the most distant point (in metric distance) from the set \{x_{i_{1}},x_{i_{2}},...,x_{i_{j-1}}\} with regard to the rest points. Compared with random sampling, it has better coverage of the entire point set given the same number of centroids. In contrast to CNNs that scan the vector space agnostic of data distribution, our sampling strategy generates receptive fields in a data dependent manner.  One issue that still remains is how to generate overlapping partitioning of a point set. Each partition is defined as a neighborhood ball in the underlying Euclidean space, whose parameters include centroid location and scale. To evenly cover the whole set, the centroids are selected among input point set by a farthest point sampling (FPS) algorithm. Compared with volumetric CNNs that scan the space with fixed strides, our local receptive fields are dependent on both the input data and the metric, and thus more efficient and effective.

Question: Is it true that FPS could cover the whole set?
[For evenly covering the whole set, the paper selects centroids among the input point set by a farthest point sampling (FPS) algorithm. Given input points \{x_{1},x_{2},...,x_{n}\}, the paper uses iterative FPS to choose a subset of points \{x_{i_{1}},x_{i_{2}},...,x_{i_{m}}\}, such that x_{i_{j}} is the most distant point (in metric distance) from the set \{x_{i_{1}},x_{i_{2}},...,x_{i_{j-1}}\} with regard to the rest points. Compared with random sampling, it has better coverage of the entire point set given the same number of centroids.]