What is the difference between a R-tree and a BVH?
Note that we want to be able to retrieve, for any query range, the points that are inside, or sometimes the points that are closest to that query range. That's why a bounding-volume hierarchy is useful.

However, bounding-volume hierarchy is a very general notion. When designing a bounding-volume hierarchy data structure in practice, it is extremely important to decide what kind of bounding volumes to use. The bounding volume can be any geometric shape: circle, hexagon, square, trangle or rectangle. It is important to keep this bounding volume very simple (we want it to be easy to store, rather than a complicated geometrical shape) and easy to fit other objects in (we don't want to waste a lot of free space in the bounding volume, as it can cause exhaustive search.

Quote from [1],


  Below, I will first explain what a bounding-volume hierarchy is and
  how it is used. After that, I will explain what issues have to be
  addressed when designing a bounding-volume hierarchy. I will then
  focus on a particular class of bounding volume hierarchies, namely
  R-trees, and give an overview of our results on Rtrees.


R-tree is a class of BVH data structures that uses rectangles as bounding-volume. Rectangles are easy to store and they can cover other shapes easily. It is noted that R-tree is not the only class of BVH and there are other BVHs proposed using different shapes. Even among R-trees, there is a variety.

This data structure is important for different communities: computer graphics, computational geometry, database and others. All of them need to deal with situations where they need to query a set of geometrical objects (or vectors).

[1] Herman Johannes Haverkort, Results on geometric networks and data structures, 2004.