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1Tecnalia - Basque Research and Technology Alliance (BRTA) |
2Computer Vision Center - Universitat Autònoma de Barcelona |
3Nankai University, Tianjin, China |
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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.
Aitor Alvarez-Gila, Joost van de Weijer, Yaxing Wang, Estibaliz Garrote MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation IEEE International Conference on Image Processing (ICIP), 2022 [bibtex] |
Contact: Aitor Alvarez-Gila