Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions   Scene understanding has been of high interest in computer vision. It
encompasses not only identifying objects in a scene, but also their
relationships within the given context. With this goal, a recent line of works
tackles 3D semantic segmentation and scene layout prediction. In our work we
focus on scene graphs, a data structure that organizes the entities of a scene
in a graph, where objects are nodes and their relationships modeled as edges.
We leverage inference on scene graphs as a way to carry out 3D scene
understanding, mapping objects and their relationships. In particular, we
propose a learned method that regresses a scene graph from the point cloud of a
scene. Our novel architecture is based on PointNet and Graph Convolutional
Networks (GCN). In addition, we introduce 3DSSG, a semi-automatically generated
dataset, that contains semantically rich scene graphs of 3D scenes. We show the
application of our method in a domain-agnostic retrieval task, where graphs
serve as an intermediate representation for 3D-3D and 2D-3D matching.

9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas