| Περίληψη: | In the current Master thesis, we address the scene generation task, with our
main focus being on indoor scenes synthesis. Existing approaches pose the
scene generation task as a layout creation problem. Namely the task is to
populate a scene with a set of labelled bounding boxes that correspond to a
set of furniture pieces. In particular, these methods typically seek to learn
a probability distribution over a set of attributes that define each object
such as their shape, category, orientation and position in the scene. During
generation, the generated bounding boxes are replaced with 3D objects
by retrieving them from a library of assets based on various criteria such
as size, object category etc. Naturally, since the object retrieval process is
independent from the layout generation there are no guarantees that the
generated objects will be coherent in terms of style and appearance. To this
end, in this work, we propose a novel generative model for indoor scenes
that takes into consideration the per-object style during the generation
process. We believe that this is a crucial step towards generating realistic
environments. In particular, we build on top of ATISS [18], which is the
state-of-the-art indoor scene generation pipeline. Specifically, we extend
its capabilities by also incorporating a style prediction module. Further-
more, we also propose a novel retrieval procedure that instead of simply
relying on the size to replace bounding boxes with 3D models, takes into
account the per-object style. Our experimental evaluation showcases that
our model consistently generates stylistically meaningful scenes, (i.e. the
nightstands next two a bed or the chairs around the table should have
similar appearance), while performing on par with ATISS wrt. the scene
generation quality. Finally, we also introduce various metrics that can be
used for evaluating the generated scenes in terms of the style coherence.
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