MVMO: A MULTI-OBJECT DATASET FOR WIDE BASELINE MULTI-VIEW SEMANTIC SEGMENTATION
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2022
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IEEE Computer Society
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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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Alvarez-Gila , A , van de Weijer , J , Wang , Y & Garrote , E 2022 , MVMO : A MULTI-OBJECT DATASET FOR WIDE BASELINE MULTI-VIEW SEMANTIC SEGMENTATION . in 2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings . Proceedings - International Conference on Image Processing, ICIP , IEEE Computer Society , pp. 1166-1170 , 29th IEEE International Conference on Image Processing, ICIP 2022 , Bordeaux , France , 16/10/22 . https://doi.org/10.1109/ICIP46576.2022.9897955
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