Let's say I have two sets of points:

```
>>> points1.shape
(10000, 3)
>>> points2.shape
(1529, 3)

```

And I want a list of indices of `points1` within a euclidean distance `cutoff` of a point in `points2`. I could easily do this using `scipy.spatial.distance.cdist` like so:

```
from scipy.spatial.distance import cdist
import numpy

indices = numpy.argwhere(cdist(points1, points2).min(axis=0) < cutoff)

```

However this seems inefficient since I don't need to know how far away points are from each other, just whether they're within a cutoff distance. Could a KDTree help with this?