  We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of
116,000 scenes containing randomly placed objects of 10 distinct classes and
captured from 25 camera locations in the upper hemisphere. MVMO comprises
photorealistic, path-traced image renders, together with semantic segmentation
ground truth for every view. Unlike existing multi-view datasets, MVMO features
wide baselines between cameras and high density of objects, which lead to large
disparities, heavy occlusions and view-dependent object appearance. Single view
semantic segmentation is hindered by self and inter-object occlusions that
could benefit from additional viewpoints. Therefore, we expect that MVMO will
propel research in multi-view semantic segmentation and cross-view semantic
transfer. We also provide baselines that show that new research is needed in
such fields to exploit the complementary information of multi-view setups.
