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

Source: [MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6ca21e209131cea35161>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:15:46.115Z

Authors: Aitor Alvarez-Gila, Joost van de Weijer, Yaxing Wang, Estibaliz Garrote

Published: 2022-05-30T22:37:43.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.15452>)

[PDF](<https://arxiv.org/pdf/2205.15452.pdf>)