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Unsupervised representation learning techniques, such as learning wordembeddings, have had a significant impact on the field of natural languageprocessing. Similar representation learning techniques have not yet becomecommonplace in the context of 3D vision. This, despite the fact that thephysical 3D spaces have a similar semantic structure to bodies of text: wordsare surrounded by words that are semantically related, just like objects aresurrounded by other objects that are similar in concept and usage.  In this work, we exploit this structure in learning semantically meaningfullow dimensional vector representations of objects. We learn these vectorrepresentations by mining a dataset of scanned 3D spaces using an unsupervisedalgorithm. We represent objects as point clouds, a flexible and generalrepresentation for 3D data, which we encode into a vector representation. Weshow that using our method to include context increases the ability of aclustering algorithm to distinguish different semantic classes from each other.Furthermore, we show that our algorithm produces continuous and meaningfulobject embeddings through interpolation experiments.

Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds