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We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of116,000 scenes containing randomly placed objects of 10 distinct classes andcaptured from 25 camera locations in the upper hemisphere. MVMO comprisesphotorealistic, path-traced image renders, together with semantic segmentationground truth for every view. Unlike existing multi-view datasets, MVMO featureswide baselines between cameras and high density of objects, which lead to largedisparities, heavy occlusions and view-dependent object appearance. Single viewsemantic segmentation is hindered by self and inter-object occlusions thatcould benefit from additional viewpoints. Therefore, we expect that MVMO willpropel research in multi-view semantic segmentation and cross-view semantictransfer. We also provide baselines that show that new research is needed insuch fields to exploit the complementary information of multi-view setups.

MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation