  Unsupervised representation learning techniques, such as learning word
embeddings, have had a significant impact on the field of natural language
processing. Similar representation learning techniques have not yet become
commonplace in the context of 3D vision. This, despite the fact that the
physical 3D spaces have a similar semantic structure to bodies of text: words
are surrounded by words that are semantically related, just like objects are
surrounded by other objects that are similar in concept and usage.
  In this work, we exploit this structure in learning semantically meaningful
low dimensional vector representations of objects. We learn these vector
representations by mining a dataset of scanned 3D spaces using an unsupervised
algorithm. We represent objects as point clouds, a flexible and general
representation for 3D data, which we encode into a vector representation. We
show that using our method to include context increases the ability of a
clustering algorithm to distinguish different semantic classes from each other.
Furthermore, we show that our algorithm produces continuous and meaningful
object embeddings through interpolation experiments.
