%0 Generic
%A Alvarez-Gila, Aitor
%A van de Weijer, Joost
%A Wang, Yaxing
%A Garrote, Estibaliz
%T MVMO: 												A MULTI-OBJECT DATASET FOR WIDE BASELINE MULTI-VIEW SEMANTIC SEGMENTATION
%J Proceedings - International Conference on Image Processing, ICIP
%D 2022
%@ 1522-4880
%U https://hdl.handle.net/11556/2498
%X 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.
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